HTML5 Jobs in Lebanon

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <span>Please submit your CV in English and indicate your level of English proficiency.<br> Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems.<br> Participation is project-based, not permanent employment.<br>What this opportunity involves: We're building a dataset to evaluate AI coding agents - how well a model handles real-world developer tasks.<br> You'll create challenging tasks and evaluation criteria within realistic simulated environments: Build realistic developer environments - a virtual company with codebase, infrastructure, and context (tickets, docs, conversations) that forms a believable development history Design tasks from intermediate states of these environments - craft the prompt, define what "solved" means, and ensure the task is solvable by an AI agent Write tests that verify agent solutions - accept all valid approaches and reject incorrect ones, neither too strict nor too lenient Iterate on tasks and tests based on QA feedback - review agent solutions, analyze failures, and refine until the evaluation is fair and robust What this is NOT: Not data labeling Not prompt engineering Not writing code from scratch - the agent writes most of the code; you guide and evaluate What we look for: 8+ years in software development Core stack: Python (FastAPI), JavaScript/TypeScript (React), Docker, Postgres, Kafka, Redis Experience writing tests (functional, integration) English proficiency - B2+ Why this is hard: Frontier models are already good at coding.<br> Creating a task that genuinely challenges the best models is non-trivial.<br> You need to deeply understand where models fail and what scenarios reveal the difference between a good and a bad solution.<br> Tasks have many valid solutions - writing tests that accept all correct solutions and reject incorrect ones is harder than it sounds.<br> How it works Apply → Pass qualification(s) → Join a project → Complete tasks → Get paidEffort estimate Tasks for this project are estimated to take 30 hours to complete, depending on complexity.<br> This is an estimate and not a schedule requirement; you choose when and how to work.<br> Tasks must be submitted by the deadline and meet the listed acceptance criteria to be accepted.<br> Compensation: Up to $150/hr equivalent , depending on level and pace.<br> Tasks are estimated at ~30 hours each; you set your own schedule.<br></span> </div>

Role Overview <br>Build production AI systems for clients. You'll design and ship agentic workflows on Claude and AWS Bedrock AgentCore, retrieval systems backed by Bedrock Knowledge Bases, and MCP-based automations that connect directly into client tool stacks (Notion, Linear, HubSpot, AWS, Google Workspace, and more). This is a hands-on build role. You will write code, deploy infrastructure, and sit in client rooms explaining why the system behaves the way it does. <br>Tasks & Responsibilities <br>Design and build applied AI systems on Claude and AWS Bedrock AgentCore: RAG pipelines backed by Bedrock Knowledge Bases, agentic workflows, and multi-tool orchestration via MCP. <br>Own delivery of AI components end-to-end: architecture, implementation, evaluation, and post-launch monitoring. <br>Integrate Claude-based agents with client infrastructure and business tools via MCP servers (HubSpot, Notion, Linear, Google Workspace). <br>Translate ambiguous client problems into scoped technical builds with clear success criteria and numbers attached. <br>Document architecture and decisions in the client's Notion record so infra details never live only in someone's head. <br>Work alongside Pre-Sales Engineers during scoping calls to validate technical feasibility before commitments are made. <br>Select and tune the right model, retrieval strategy, and Knowledge Base configuration per use case. <br>Build and maintain eval suites (offline test sets, regression checks, LLM-as-judge scoring) that catch quality drift before a client does. <br>Design agentic control flow: tool-selection logic, retry/fallback paths, and guardrails against runaway loops or unsafe tool calls. <br>Instrument production systems with logging, tracing, and cost/latency dashboards to debug a bad output from a client. <br>Write modular infrastructure as code (Terraform) for AI workloads on AWS. <br>Conduct architecture reviews on other engineers' AI builds and flag failure modes before they ship. <br>Competencies <br>Can explain to a client, in plain language, why an agent made the tool call it made <br>Comfortable debugging a production LLM failure under time pressure, not just in a notebook <br>Can wire an MCP server into an agent and debug why a tool call didn't fire <br>Education, Experience & Language <br>Master's degree in Computer Science or equivalent <br>Previous experience building and shipping production ML/AI systems <br>Fluent in English and Arabic <br>Tools & Certifications <br>Strong Python engineering; comfort with AWS <br>Practical experience with LLM application patterns: RAG, agentic tool-use via MCP, prompt engineering, evaluation design <br>Hands-on experience with AWS Bedrock AgentCore and Bedrock Knowledge Bases <br>Nice to have: AWS Certified Machine Learning Engineer – Associate, AWS Certified Generative AI Developer – Professional, AWS Certified Solutions Architect – Associate or Professional, AWS Certified Developer – Associate, AWS Certified Security – Specialty (relevant given our compliance posture) <br>To Apply visit: https://digicosolutions.bamboohr.com/careers/33

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <span>Please submit your CV in English and indicate your level of English proficiency.<br> Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems.<br> Participation is project-based, not permanent employment.<br> About the Role You’ll design coding tasks that challenge frontier AI coding agents.<br> Each task is a self-contained Docker environment with a broken piece of software; an AI agent attempts the fix; automated tests verify the outcome.<br> Your deliverable is the full task package: broken code, tests, instructions, and a reference solution proving the task is solvable.<br> Responsibilities : Invent a realistic developer scenario — a real bug, a broken ETL, a missing feature — not a toy problem.<br> Build a reproducible Docker environment with pinned dependencies.<br> Write a pytest that verifies outcomes, not specific commands — deterministic, non-flaky, and does not leak the fix.<br> Write an instruction.<br>md that reads like a Jira ticket a developer would receive.<br> Write a reference solve.<br>sh proving the task is solvable.<br> Calibrate difficulty so current state-of-the-art agents solve the task 20–60% of the time.<br> Iterate based on feedback from expert QA reviewers.<br> Later: review other authors’ tasks as a QA reviewer.<br> Not in scope Data labeling, prompt engineering.<br> Production code to ship — you design problems and verification for AI agents.<br> Leetcode puzzles — scenarios must look like real developer work.<br> Not every candidate task ships — quality over quantity.<br> Requirements 3+ years of production software development in one backend stack — Python, Go, Node.<br>js, Java, or Rust.<br> Depth in one stack beats breadth.<br> Python + pytest fluency — required regardless of primary stack.<br> The task harness is pytest-based even when the broken app is in another language.<br> Fixtures, parametrize, monkeypatch, timeouts, conftest.<br>py. Docker authoring — reproducible Dockerfiles, pinned dependencies, multi-stage builds when needed, non-root user.<br> Linux &amp; Bash — comfort debugging inside containers (strace, lsof, journalctl); shell beyond set -euo pipefail.<br> AI coding agent experience — Claude Code, Cursor, Roo Code, or similar, on non-trivial work.<br> You can cite a specific time the AI was confidently wrong and how you caught it.<br> English — B2+ written.<br> Not a fit Data Science, ML, or Computer Vision engineers without backend-engineering output.<br> Manual QA testers without automation or test authoring.<br> Frontend-only, low-code / no-code, IT Support, or Business Analysts.<br> Engineers who have never written pytest from scratch.<br> Junior, intern, or assistant as the most recent role.<br> Preferred qualifications Domain depth in Security, System Administration (nginx / systemd / cron), Scientific Computing (NumPy / PyTorch / SciPy), DevOps, or Git internals.<br> Modern Python tooling (uv, poetry, pyproject.<br>toml). Coverage tooling (pytest-cov, coverage.<br>py, gcov, llvm-cov, kcov).<br> Fuzzing or property-based testing (Hypothesis).<br> Prior contribution to agent-evaluation benchmarks or related frameworks.<br> Process Apply → Pass qualification (90-minute sample-task screen + short behavioral interview) → Join a project → Complete tasks → Get paid.<br> Time commitment Onboarding: ~10 hours per first task.<br> Steady state: ~5 hours per task, 2–4 parallel tasks per author.<br> Realistic weekly load: 8–20 hours.<br> Higher volume available for top performers.<br> You choose when and how to contribute; tasks must be submitted by the deadline and meet acceptance criteria.<br> Compensation: Paid contributions, rates up to $35/hour *.<br> Task-based compensation equivalent to hourly rate, depending on performance and volume.<br> Some projects include incentive payments.<br> *Rates vary based on expertise, skills assessment, location, project needs, and other factors.<br> Higher rates may be provided to highly specialized experts.<br> Lower rates may apply during onboarding or non-core project phases.<br> Payment details are shared per project.<br> Apply Submit your CV via the Mindrift platform.<br> Indicate your English level, note this role (Software Engineering Evaluation Specialist — Terminal Bench), and include a GitHub profile link if available.<br></span> </div>

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <span>Please submit your CV in English and indicate your level of English proficiency.<br> Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems.<br> Participation is project-based, not permanent employment.<br> What this opportunity involves We're building a dataset to evaluate AI coding agents - how well a model handles real-world developer tasks.<br> You'll create challenging tasks and evaluation criteria within realistic simulated environments: Build realistic developer environments - a virtual company with codebase, infrastructure, and context (tickets, docs, conversations) that forms a believable development history Design tasks from intermediate states of these environments - craft the prompt, define what "solved" means, and ensure the task is solvable by an AI agent Write tests that verify agent solutions - accept all valid approaches and reject incorrect ones, neither too strict nor too lenient Iterate on tasks and tests based on QA feedback - review agent solutions, analyze failures, and refine until the evaluation is fair and robust What this is NOT Not data labeling Not prompt engineering Not writing code from scratch - the agent writes most of the code; you guide and evaluate What we look for 5+ years in software development Core stack: Python (FastAPI), JavaScript/TypeScript (React), Docker, Postgres, Kafka, Redis Experience writing tests (functional, integration) English proficiency - B2+ Why this is hard Frontier models are already good at coding.<br> Creating a task that genuinely challenges the best models is non-trivial.<br> You need to deeply understand where models fail and what scenarios reveal the difference between a good and a bad solution.<br> Tasks have many valid solutions - writing tests that accept all correct solutions and reject incorrect ones is harder than it sounds.<br> How it works Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid Compensation Up to $40/hr equivalent , depending on level and pace.<br> Tasks are estimated at ~20 hours each; you set your own schedule.<br></span> </div>

<section><p class="heading jdMain">Job Description</p><p class="heading">Roles &amp; Responsibilities</p><div class="paragraph"><p> <br> </p> <p> <span>The Company</span> </p> <p>Toters is an on-demand e-commerce and delivery platform that enables customers to get anything in their city with the highest level of convenience.</p> <p>Technology is at the heart of everything we do. Our product and engineering teams work every day to build experiences that make customers lives easier while continuously improving internal systems to deliver faster and at the best cost.</p> <p>If you re excited about working in a high-growth startup environment and want to be part of a team shaping the future of how people shop in the Middle East, we d love to hear from you.</p> <p> <br> </p> <p> <span>About the Role</span> </p> <p>Are you passionate about building scalable backend systems while owning the cloud infrastructure that powers them? As a <span>Senior Backend Engineer (Platform &amp; DevOps) at Toters, you'll split your time between designing and developing production-grade backend services and improving the cloud platform, deployment pipelines, and operational excellence that enable our engineering teams to deliver software safely and efficiently.</span> </p> <p>In this role, you'll own services end-to-end from application design and implementation to deployment, monitoring, scalability, security, and production reliability. Working closely with Software Engineers, Product Managers, and Infrastructure teams, you'll help build resilient systems that power our products while continuously improving the developer experience.</p> <p> <span>In this role, you will:</span> </p> <p> <br> </p> <p> <span>Backend Engineering</span> </p> <ul> <li>Collaborate with cross-functional teams to translate business requirements into scalable backend solutions.</li> <li>Design, develop, test, and maintain high-performance backend services using PHP (Laravel).</li> <li>Design and build RESTful APIs and microservices that power customer-facing and internal applications.</li> <li>Write clean, maintainable, and well-tested code following engineering best practices.</li> <li>Optimize application performance, database queries, caching strategies, and system scalability.</li> <li>Participate in architecture discussions and contribute to technical decisions that improve long-term maintainability.</li> <li>Conduct thoughtful code reviews and mentor engineers through technical guidance and best practices.</li> </ul> <p> <span>Platform &amp; DevOps</span> </p> <ul> <li>Design, build, and maintain cloud infrastructure on AWS to support highly available and scalable applications.</li> <li>Build and improve CI/CD pipelines that enable fast, reliable, and secure software delivery.</li> <li>Manage containerized applications using Docker and Kubernetes.</li> <li>Automate infrastructure provisioning and configuration using Infrastructure as Code (Terraform or similar tools).</li> <li>Improve system observability through monitoring, logging, alerting, and performance dashboards.</li> <li>Optimize cloud infrastructure for scalability, reliability, security, and cost efficiency.</li> <li>Participate in production incident response, troubleshooting, root cause analysis, and post-incident reviews.</li> <li>Implement security best practices for applications, infrastructure, secrets management, and access control.</li> <li>Continuously improve deployment processes, operational tooling, and developer experience across engineering teams.</li> </ul> <p> <br> </p> <p> <span>Key Qualifications</span> </p> <ul> <li>Bachelor s degree in Computer Science, Engineering, or a related field.</li> <li>6+ years of experience in backend software engineering, with significant hands-on experience in cloud infrastructure and DevOps practices.</li> <li>Strong experience developing backend applications using <span>PHP (Laravel).</span> </li> <li>Solid understanding of Object-Oriented Programming (OOP), SOLID principles, design patterns, and software architecture.</li> <li>Experience designing and building RESTful APIs, distributed systems, and microservices.</li> <li>Strong experience with relational databases such as <span>PostgreSQL or MySQL, along with caching technologies such as Redis.</span> </li> <li>Strong hands-on experience with <span>AWS, including services such as EC2, ECS, Lambda, RDS, S3, IAM, CloudWatch, VPC, and SQS.</span> </li> <li>Experience building and maintaining CI/CD pipelines using GitHub Actions, GitLab CI, Jenkins, or similar tools.</li> <li>Experience working with <span>Docker and Kubernetes in production environments.</span> </li> <li>Experience using Infrastructure as Code tools such as <span>Terraform or CloudFormation.</span> </li> <li>Strong understanding of Linux systems, networking fundamentals, and cloud security best practices.</li> <li>Experience implementing monitoring, logging, and observability solutions using tools such as CloudWatch, Grafana, Prometheus, or the ELK stack.</li> <li>Experience owning production systems, participating in on-call rotations, and resolving production incidents.</li> <li>Strong communication skills with the ability to collaborate across teams and mentor engineers.</li> <li>Passion for building scalable, secure, and reliable systems while continuously improving engineering excellence.</li> </ul> <p> <br> </p> <p> <span>Nice to Have</span> </p> <ul> <li>Experience with <span>Go, Python, or additional backend programming languages.</span> </li> <li>Experience with messaging and streaming technologies such as <span>Kafka, RabbitMQ, or AWS SQS/SNS.</span> </li> <li>Experience deploying serverless applications using AWS Lambda.</li> <li>Experience with Helm, ArgoCD, or GitOps workflows.</li> <li>Experience building Internal Developer Platforms (IDPs) or improving developer tooling.</li> <li>Experience with service mesh technologies such as Istio or Linkerd.</li> <li>Experience working in high-growth technology companies, e-commerce, or on-demand delivery platforms.</li> <li>AWS certifications are a plus.</li> </ul> <p> <br> </p> <p> <span>Why Toters?</span> </p> <ul> <li>Flexible work environment with hybrid-friendly roles.</li> <li>Opportunity to work across software engineering and cloud infrastructure, building systems that directly power Toters' products.</li> <li>Solve complex engineering challenges involving backend architecture, cloud infrastructure, automation, and distributed systems.</li> <li>Collaborate with talented engineers in a culture of ownership, mentorship, and continuous learning.</li> <li>Direct impact on products used by thousands of customers every day.</li> <li>Competitive compensation package.</li> <li>Exclusive discounts on Toters orders.</li> <li>First-class medical insurance.</li> </ul></div></section>

<p>Role Overview Help building, operating, and continuously improving the platforms and infrastructure that support our customers and consulting engagements on AWS. You will work closely with engineering teams to improve software delivery, infrastructure reliability, security, observability, scalability, and developer experience. The role combines AWS cloud infrastructure, Infrastructure as Code, CI/CD, container orchestration, automation, monitoring, and operational excellence. We are looking for someone who is comfortable taking ownership, solving problems systematically, automating repetitive work, and collaborating across engineering teams. Tasks &amp; Responsibilities Design, build, operate, and continuously improve infrastructure and platform services on AWS. Develop and maintain Infrastructure as Code to ensure consistent, repeatable, and auditable infrastructure provisioning. Build and improve CI/CD pipelines to enable reliable, secure, and efficient software delivery. Deploy and operate containerized applications using container orchestration platforms, primarily Amazon ECS. Build and maintain container-based deployment workflows using technologies such as Docker and Amazon ECR. Automate infrastructure provisioning, application deployments, configuration, and operational processes. Implement and maintain monitoring, logging, alerting, and observability solutions. Improve platform reliability, availability, scalability, performance, and resilience. Support engineering teams in deploying, operating, and troubleshooting applications running on AWS. Participate in incident response, root cause analysis, and continuous improvement activities. Identify operational risks, bottlenecks, and repetitive manual processes and propose improvements. Apply security best practices across AWS infrastructure, containerized workloads, CI/CD pipelines, secrets, access control, networking, and platform services. Contribute to AWS cost visibility, resource optimization, and cloud efficiency. Maintain clear technical documentation, operational procedures, runbooks, and architecture documentation. Collaborate with software engineers, architects, security teams, and other stakeholders to improve engineering standards and practices. Evaluate tools and technologies based on reliability, maintainability, security, scalability, operational complexity, and business value. Competencies Strong understanding of AWS cloud infrastructure and modern infrastructure engineering practices. Hands-on experience designing, deploying, or operating workloads on AWS. Experience with container technologies and container orchestration platforms, particularly Amazon ECS. Experience with Docker and containerized application delivery. Understanding of core AWS services and concepts including compute, networking, storage, identity and access management, security, monitoring, and highly available architectures. Experience with Infrastructure as Code tools such as Terraform, AWS CloudFormation, AWS CDK, or similar approaches. Understanding of CI/CD concepts and experience building or maintaining automated software delivery pipelines. Experience with scripting or programming for automation using languages such as Python, Bash, Go, or similar. Strong understanding of Linux systems, networking, DNS, load balancing, TLS, and common infrastructure concepts. Experience with monitoring, logging, metrics, alerting, and observability practices. Understanding of security principles including AWS IAM, secrets management, access control, vulnerability management, and secure infrastructure configuration. Ability to troubleshoot technical and operational problems across applications, infrastructure, networking, containers, and AWS services. Strong ownership mindset and ability to work collaboratively with engineering teams. Ability to communicate technical concepts clearly and document systems and processes effectively. Tools &amp; Certifications Experience with Amazon ECS and AWS-native container platforms. Familiarity with Kubernetes or Amazon EKS. Experience with GitOps practices and modern deployment automation. Experience with reliability engineering, SRE practices, SLIs, SLOs, and incident management. Experience with AWS cost management, optimization, and FinOps practices. Familiarity with AWS security services, governance controls, and security best practices. Experience building internal developer platforms or improving developer experience. Familiarity with databases, messaging platforms, caching systems, and distributed-system components. Knowledge of AWS Well-Architected principles and cloud architecture best practices.</p><p><strong>Desired Candidate Profile</strong></p><p>Previous experience in a similar role Bachelor's degree in Computer Science or related field Fluent in speaking &amp; writing, Arabic &amp; English</p>

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <span>Please submit your CV in English and indicate your level of English proficiency.<br> Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems.<br> Participation is project-based, not permanent employment.<br> What this opportunity involves We're building a dataset to evaluate AI coding agents - how well a model handles real-world developer tasks.<br> You'll create challenging tasks and evaluation criteria within realistic simulated environments: Build realistic developer environments - a virtual company with codebase, infrastructure, and context (tickets, docs, conversations) that forms a believable development history Design tasks from intermediate states of these environments - craft the prompt, define what "solved" means, and ensure the task is solvable by an AI agent Write tests that verify agent solutions - accept all valid approaches and reject incorrect ones, neither too strict nor too lenient Iterate on tasks and tests based on QA feedback - review agent solutions, analyze failures, and refine until the evaluation is fair and robust What this is NOT Not data labeling Not prompt engineering Not writing code from scratch - the agent writes most of the code; you guide and evaluate What we look for 5+ years in software development Core stack: Python (FastAPI), JavaScript/TypeScript (React), Docker, Postgres, Kafka, Redis Experience writing tests (functional, integration) English proficiency - B2+ Why this is hard Frontier models are already good at coding.<br> Creating a task that genuinely challenges the best models is non-trivial.<br> You need to deeply understand where models fail and what scenarios reveal the difference between a good and a bad solution.<br> Tasks have many valid solutions - writing tests that accept all correct solutions and reject incorrect ones is harder than it sounds.<br> How it works Apply → Pass qualification(s) → Join a project → Complete tasks → Get paid Compensation Up to $40/hr equivalent , depending on level and pace.<br> Tasks are estimated at ~20 hours each; you set your own schedule.<br></span> </div>

<p>The Senior Developer is a full-time, on-site role based in Ras Beirut. This role is responsible for designing, developing, and maintaining high-quality software solutions that address complex business challenges. Day-to-day tasks include writing clean and efficient code, implementing software features according to technical specifications, participating in code reviews and architectural discussions, troubleshooting and resolving technical issues, and collaborating with cross-functional teams to deliver robust solutions. The developer contributes to the entire software development lifecycle, from requirements analysis and design through implementation, testing, and deployment. The role also involves documenting code and technical solutions, identifying opportunities for process improvement and optimization, mentoring junior developers, and staying current with industry best practices and emerging technologies.</p><p><strong>Desired Candidate Profile</strong></p><p>Candidates should possess strong expertise in software development with proficiency in one or more programming languages such as Python, Java, C#, JavaScript, or similar.</p><p>Candidates should possess solid understanding of software design patterns, object-oriented programming principles, and best practices for writing maintainable and scalable code.</p><p>Candidates should possess robust problem-solving skills and the ability to analyze complex technical requirements and propose effective solutions.</p><p>Candidates should possess experience with version control systems such as Git and familiarity with collaborative development workflows.</p><p>Candidates should possess understanding of software testing methodologies, including unit testing, integration testing, and quality assurance practices.</p><p>Bachelor's degree in Computer Science, Software Engineering, Information Systems, or a related technical field.</p><p>Minimum 5 years of professional experience in software development or similar roles.</p><p>Experience with relational and non-relational databases and SQL or equivalent query languages.</p><p>Strong communication skills and the ability to work effectively in cross-functional teams and document technical solutions clearly.</p><p>Experience with API development and integration of third-party services is an advantage.</p><p>Familiarity with agile development methodologies and tools such as Jira or similar project management platforms.</p><p>Demonstrated ability to mentor junior developers and contribute to team knowledge sharing and professional development.</p>

<p>The Senior Developer is a full-time, on-site role based in Ras Beirut. This role is responsible for designing, developing, and maintaining high-quality software solutions that address complex business challenges. Day-to-day tasks include writing clean and efficient code, implementing software features according to technical specifications, participating in code reviews and architectural discussions, troubleshooting and resolving technical issues, and collaborating with cross-functional teams to deliver robust solutions. The developer contributes to the entire software development lifecycle, from requirements analysis and design through implementation, testing, and deployment. The role also involves documenting code and technical solutions, identifying opportunities for process improvement and optimization, mentoring junior developers, and staying current with industry best practices and emerging technologies.</p><p><strong>Desired Candidate Profile</strong></p><p>Candidates should possess strong expertise in software development with proficiency in one or more programming languages such as Python, Java, C#, JavaScript, or similar.</p><p>Candidates should possess solid understanding of software design patterns, object-oriented programming principles, and best practices for writing maintainable and scalable code.</p><p>Candidates should possess robust problem-solving skills and the ability to analyze complex technical requirements and propose effective solutions.</p><p>Candidates should possess experience with version control systems such as Git and familiarity with collaborative development workflows.</p><p>Candidates should possess understanding of software testing methodologies, including unit testing, integration testing, and quality assurance practices.</p><p>Bachelor's degree in Computer Science, Software Engineering, Information Systems, or a related technical field.</p><p>Minimum 5 years of professional experience in software development or similar roles.</p><p>Experience with relational and non-relational databases and SQL or equivalent query languages.</p><p>Strong communication skills and the ability to work effectively in cross-functional teams and document technical solutions clearly.</p><p>Experience with API development and integration of third-party services is an advantage.</p><p>Familiarity with agile development methodologies and tools such as Jira or similar project management platforms.</p><p>Demonstrated ability to mentor junior developers and contribute to team knowledge sharing and professional development.</p>

<p><h4>Please submit your CV in English and indicate your level of English proficiency.<\/h4>\n<p>Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment.<\/p>\n<h4>What this opportunity involves<\/h4>\n<p>We're building a dataset to evaluate AI coding agents - how well a model handles real-world developer tasks.<\/p>\n<p>You'll create challenging tasks and evaluation criteria within realistic simulated environments:<\/p>\n<ul>\n <li>Build realistic developer environments - a virtual company with codebase, infrastructure, and context (tickets, docs, conversations) that forms a believable development history<\/li>\n <li>Design tasks from intermediate states of these environments - craft the prompt, define what \"solved\" means, and ensure the task is solvable by an AI agent<\/li>\n <li>Write tests that verify agent solutions - accept all valid approaches and reject incorrect ones, neither too strict nor too lenient<\/li>\n <li>Iterate on tasks and tests based on QA feedback - review agent solutions, analyze failures, and refine until the evaluation is fair and robust<\/li>\n<\/ul>\n<h4>What this is not<\/h4>\n<ul>\n <li>Not data labeling<\/li>\n <li>Not prompt engineering<\/li>\n <li>Not writing code from scratch - the agent writes most of the code; you guide and evaluate<\/li>\n<\/ul>\n<h4>What we look for<\/h4>\n<ul>\n <li>5+ years in software development<\/li>\n <li>Core stack: Python (FastAPI), JavaScript\/TypeScript (React), Docker, Postgres, Kafka, Redis<\/li>\n <li>Experience writing tests (functional, integration)<\/li>\n <li>English proficiency - B2+<\/li>\n<\/ul>\n<h4>Why this is hard<\/h4>\n<p>Frontier models are already good at coding. Creating a task that genuinely challenges the best models is non-trivial. You need to deeply understand where models fail and what scenarios reveal the difference between a good and a bad solution. Tasks have many valid solutions - writing tests that accept all correct solutions and reject incorrect ones is harder than it sounds.<\/p>\n<h4>How it works<\/h4>\n<p>Apply > Pass qualification(s) > Join a project > Complete tasks > Get paid<\/p>\n<h4>Compensation<\/h4>\n<p>Up to $40\/hr equivalent, depending on level and pace. Tasks are estimated at approximately 20 hours each; you set your own schedule.<\/p><\/p><p><\/p>

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <span>Please submit your CV in English and indicate your level of English proficiency.<br> Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems.<br> Participation is project-based, not permanent employment.<br>What this opportunity involves: We're building a dataset to evaluate AI coding agents - how well a model handles real-world developer tasks.<br> You'll create challenging tasks and evaluation criteria within realistic simulated environments: Build realistic developer environments - a virtual company with codebase, infrastructure, and context (tickets, docs, conversations) that forms a believable development history Design tasks from intermediate states of these environments - craft the prompt, define what "solved" means, and ensure the task is solvable by an AI agent Write tests that verify agent solutions - accept all valid approaches and reject incorrect ones, neither too strict nor too lenient Iterate on tasks and tests based on QA feedback - review agent solutions, analyze failures, and refine until the evaluation is fair and robust What this is NOT: Not data labeling Not prompt engineering Not writing code from scratch - the agent writes most of the code; you guide and evaluate What we look for: 8+ years in software development Core stack: Python (FastAPI), JavaScript/TypeScript (React), Docker, Postgres, Kafka, Redis Experience writing tests (functional, integration) English proficiency - B2+ Why this is hard: Frontier models are already good at coding.<br> Creating a task that genuinely challenges the best models is non-trivial.<br> You need to deeply understand where models fail and what scenarios reveal the difference between a good and a bad solution.<br> Tasks have many valid solutions - writing tests that accept all correct solutions and reject incorrect ones is harder than it sounds.<br> How it works Apply → Pass qualification(s) → Join a project → Complete tasks → Get paidEffort estimate Tasks for this project are estimated to take 30 hours to complete, depending on complexity.<br> This is an estimate and not a schedule requirement; you choose when and how to work.<br> Tasks must be submitted by the deadline and meet the listed acceptance criteria to be accepted.<br> Compensation: Up to $150/hr equivalent , depending on level and pace.<br> Tasks are estimated at ~30 hours each; you set your own schedule.<br></span> </div>

<p><h4>Please submit your CV in English and indicate your level of English proficiency.<\/h4>\n<p><strong>Mindrift<\/strong> connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment.<\/p>\n<h4>What this opportunity involves<\/h4>\n<p>We're building a dataset to evaluate AI coding agents - how well a model handles real-world developer tasks.<\/p>\n<p>You'll create challenging tasks and evaluation criteria within realistic simulated environments:<\/p>\n<ul>\n <li>Build realistic developer environments - a virtual company with codebase, infrastructure, and context (tickets, docs, conversations) that forms a believable development history<\/li>\n <li>Design tasks from intermediate states of these environments - craft the prompt, define what \"solved\" means, and ensure the task is solvable by an AI agent<\/li>\n <li>Write tests that verify agent solutions - accept all valid approaches and reject incorrect ones, neither too strict nor too lenient<\/li>\n <li>Iterate on tasks and tests based on QA feedback - review agent solutions, analyze failures, and refine until the evaluation is fair and robust<\/li>\n<\/ul>\n<h4>What this is not<\/h4>\n<ul>\n <li>Not data labeling<\/li>\n <li>Not prompt engineering<\/li>\n <li>Not writing code from scratch - the agent writes most of the code; you guide and evaluate<\/li>\n<\/ul>\n<h4>What we look for<\/h4>\n<ul>\n <li>5+ years in software development<\/li>\n <li>Core stack: Python (FastAPI), JavaScript\/TypeScript (React), Docker, Postgres, Kafka, Redis<\/li>\n <li>Experience writing tests (functional, integration)<\/li>\n <li>English proficiency - B2+<\/li>\n<\/ul>\n<h4>Why this is hard<\/h4>\n<p>Frontier models are already good at coding. Creating a task that genuinely challenges the best models is non-trivial. You need to deeply understand where models fail and what scenarios reveal the difference between a good and a bad solution. Tasks have many valid solutions - writing tests that accept all correct solutions and reject incorrect ones is harder than it sounds.<\/p>\n<h4>How it works<\/h4>\n<p>Apply? Pass qualification(s)? Join a project? Complete tasks? Get paid.<\/p>\n<h4>Compensation<\/h4>\n<p>Up to $40\/hr equivalent, depending on level and pace. Tasks are estimated at approximately 20 hours each; you set your own schedule.<\/p><\/p><p><\/p>

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <span>Please submit your CV in English and indicate your level of English proficiency.<br> Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems.<br> Participation is project-based, not permanent employment.<br>What this opportunity involves: We're building a dataset to evaluate AI coding agents - how well a model handles real-world developer tasks.<br> You'll create challenging tasks and evaluation criteria within realistic simulated environments: Build realistic developer environments - a virtual company with codebase, infrastructure, and context (tickets, docs, conversations) that forms a believable development history Design tasks from intermediate states of these environments - craft the prompt, define what "solved" means, and ensure the task is solvable by an AI agent Write tests that verify agent solutions - accept all valid approaches and reject incorrect ones, neither too strict nor too lenient Iterate on tasks and tests based on QA feedback - review agent solutions, analyze failures, and refine until the evaluation is fair and robust What this is NOT: Not data labeling Not prompt engineering Not writing code from scratch - the agent writes most of the code; you guide and evaluate What we look for: 8+ years in software development Core stack: Python (FastAPI), JavaScript/TypeScript (React), Docker, Postgres, Kafka, Redis Experience writing tests (functional, integration) English proficiency - B2+ Why this is hard: Frontier models are already good at coding.<br> Creating a task that genuinely challenges the best models is non-trivial.<br> You need to deeply understand where models fail and what scenarios reveal the difference between a good and a bad solution.<br> Tasks have many valid solutions - writing tests that accept all correct solutions and reject incorrect ones is harder than it sounds.<br> How it works Apply → Pass qualification(s) → Join a project → Complete tasks → Get paidEffort estimate Tasks for this project are estimated to take 30 hours to complete, depending on complexity.<br> This is an estimate and not a schedule requirement; you choose when and how to work.<br> Tasks must be submitted by the deadline and meet the listed acceptance criteria to be accepted.<br> Compensation: Up to $150/hr equivalent , depending on level and pace.<br> Tasks are estimated at ~30 hours each; you set your own schedule.<br></span> </div>

<p>Aspire Software is looking for a Power BI Developer to join our team in Lebanon.</p> <p> <strong>Here is a little window into our company:</strong> Aspire Software operates and manages wholly owned software companies, providing mission-critical solutions across multiple verticals. By implementing industry best practices, Aspire delivers a time sensitive integration process, and the operation of a decentralized model has allowed it to become a hub for creating rapid growth by reinvesting in its portfolio.</p> <p> </p> <p>We are looking for a Power BI Developer to join our team and contribute to the development and enhancement of our business intelligence and reporting platform. The role will focus on designing, developing, and maintaining interactive Power BI dashboards and reports that enable clients to analyze their business performance, monitor KPIs, and make</p> <p>data-driven decisions.</p> <p>You will work closely with Product, Development, Data, and Business teams to translate business and reporting requirements into scalable, user-friendly Power BI solutions.</p> <p>Key Responsibilities</p> <ul> <li>Design, develop, and maintain Power BI dashboards and reports within the Lumina platform.</li> <li>Build and optimize Power BI data models, relationships, measures, and calculated columns.</li> <li>Work with large and complex datasets and ensure reports are accurate, performant, and scalable.</li> <li>Develop reporting solutions based on business requirements, including: KPI dashboards,</li> <li>Performance reports, Trend and comparison analysis, Market and brand analysis, Year-over-year and period comparisons</li> <li>Optimize Power BI reports and datasets to ensure good performance and efficient loading times.</li> <li>Work with the development team to integrate Power BI reporting into the Lumina application.</li> <li>Support the implementation of new reporting features and enhancements within Lumina.</li> <li>Collaborate with Product Managers and stakeholders to understand requirements and translate them into technical reporting solutions.</li> </ul> <p> </p><p><strong>Desired Candidate Profile</strong></p><ul> <li>2+ years of professional experience with Power BI development.</li> <li>Strong knowledge of Power BI Desktop and Power BI Service.</li> <li>Strong experience with Power BI data modeling.</li> <li>Good understanding of Power Query / M.</li> <li>Strong knowledge of data modeling concepts, including relationships, star schemas, dimensions, and fact tables.</li> <li>Experience working with SQL databases and writing SQL queries.</li> <li>Experience developing interactive dashboards and reports for business users.</li> <li>Strong analytical and problem-solving skills.</li> <li>Ability to understand business requirements and translate them into reporting solutions.</li> <li>Good understanding of KPI calculations and business reporting concepts.</li> <li>Ability to troubleshoot and optimize Power BI reports and data models.</li> </ul> <p> </p>

<p><h4>Please submit your CV in English and indicate your level of English proficiency.<\/h4>\n<p>Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment.<\/p>\n<h4>What this opportunity involves:<\/h4>\n<p>We're building a dataset to evaluate AI coding agents - how well a model handles real-world developer tasks.<\/p>\n<p>You'll create challenging tasks and evaluation criteria within realistic simulated environments:<\/p>\n<ul>\n <li>Build realistic developer environments - a virtual company with codebase, infrastructure, and context (tickets, docs, conversations) that forms a believable development history<\/li>\n <li>Design tasks from intermediate states of these environments - craft the prompt, define what \"solved\" means, and ensure the task is solvable by an AI agent<\/li>\n <li>Write tests that verify agent solutions - accept all valid approaches and reject incorrect ones, neither too strict nor too lenient<\/li>\n <li>Iterate on tasks and tests based on QA feedback - review agent solutions, analyze failures, and refine until the evaluation is fair and robust<\/li>\n<\/ul>\n<h4>What this is NOT:<\/h4>\n<ul>\n <li>Not data labeling<\/li>\n <li>Not prompt engineering<\/li>\n <li>Not writing code from scratch - the agent writes most of the code; you guide and evaluate<\/li>\n<\/ul>\n<h4>What we look for:<\/h4>\n<ul>\n <li>8+ years in software development<\/li>\n <li>Core stack: Python (FastAPI), JavaScript\/TypeScript (React), Docker, Postgres, Kafka, Redis<\/li>\n <li>Experience writing tests (functional, integration)<\/li>\n <li>English proficiency - B2+<\/li>\n<\/ul>\n<h4>Why this is hard:<\/h4>\n<p>Frontier models are already good at coding. Creating a task that genuinely challenges the best models is non-trivial. You need to deeply understand where models fail and what scenarios reveal the difference between a good and a bad solution. Tasks have many valid solutions - writing tests that accept all correct solutions and reject incorrect ones is harder than it sounds.<\/p>\n<h4>How it works<\/h4>\n<p>Apply? Pass qualification(s)? Join a project? Complete tasks? Get paid.<\/p>\n<h4>Effort estimate<\/h4>\n<p>Tasks for this project are estimated to take 30 hours to complete, depending on complexity. This is an estimate and not a schedule requirement; you choose when and how to work. Tasks must be submitted by the deadline and meet the listed acceptance criteria to be accepted.<\/p>\n<h4>Compensation:<\/h4>\n<p>Up to $150\/hr equivalent, depending on level and pace. Tasks are estimated at approximately 30 hours each; you set your own schedule.<\/p><\/p><p><\/p>

<p><h4>Please submit your CV in English and indicate your level of English proficiency.<\/h4>\n<p>Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment.<\/p>\n<h4>What this opportunity involves:<\/h4>\n<p>We're building a dataset to evaluate AI coding agents - how well a model handles real-world developer tasks.<\/p>\n<p>You'll create challenging tasks and evaluation criteria within realistic simulated environments:<\/p>\n<ul>\n<li>Build realistic developer environments - a virtual company with codebase, infrastructure, and context (tickets, docs, conversations) that forms a believable development history<\/li>\n<li>Design tasks from intermediate states of these environments - craft the prompt, define what \"solved\" means, and ensure the task is solvable by an AI agent<\/li>\n<li>Write tests that verify agent solutions - accept all valid approaches and reject incorrect ones, neither too strict nor too lenient<\/li>\n<li>Iterate on tasks and tests based on QA feedback - review agent solutions, analyze failures, and refine until the evaluation is fair and robust<\/li>\n<\/ul>\n<h4>What this is not:<\/h4>\n<ul>\n<li>Not data labeling<\/li>\n<li>Not prompt engineering<\/li>\n<li>Not writing code from scratch - the agent writes most of the code; you guide and evaluate<\/li>\n<\/ul>\n<h4>What we look for:<\/h4>\n<ul>\n<li>8+ years in software development<\/li>\n<li>Core stack: Python (FastAPI), JavaScript\/TypeScript (React), Docker, Postgres, Kafka, Redis<\/li>\n<li>Experience writing tests (functional, integration)<\/li>\n<li>English proficiency - B2+<\/li>\n<\/ul>\n<h4>Why this is hard:<\/h4>\n<p>Frontier models are already good at coding. Creating a task that genuinely challenges the best models is non-trivial. You need to deeply understand where models fail and what scenarios reveal the difference between a good and a bad solution. Tasks have many valid solutions - writing tests that accept all correct solutions and reject incorrect ones is harder than it sounds.<\/p>\n<h4>How it works<\/h4>\n<p>Apply? Pass qualification(s)? Join a project? Complete tasks? Get paid.<\/p>\n<h4>Effort estimate<\/h4>\n<p>Tasks for this project are estimated to take 30 hours to complete, depending on complexity. This is an estimate and not a schedule requirement; you choose when and how to work. Tasks must be submitted by the deadline and meet the listed acceptance criteria to be accepted.<\/p>\n<h4>Compensation:<\/h4>\n<p>Up to $150\/hr equivalent, depending on level and pace. Tasks are estimated at approximately 30 hours each; you set your own schedule.<\/p><\/p><p><\/p>

<p><h4>Please submit your CV in English and indicate your level of English proficiency.<\/h4>\n<p>Mindrift connects specialists with project-based AI opportunities for leading tech companies, focused on testing, evaluating, and improving AI systems. Participation is project-based, not permanent employment.<\/p>\n<h4>What this opportunity involves:<\/h4>\n<p>We're building a dataset to evaluate AI coding agents - how well a model handles real-world developer tasks.<\/p>\n<p>You'll create challenging tasks and evaluation criteria within realistic simulated environments:<\/p>\n<ul>\n <li>Build realistic developer environments - a virtual company with codebase, infrastructure, and context (tickets, docs, conversations) that forms a believable development history<\/li>\n <li>Design tasks from intermediate states of these environments - craft the prompt, define what \"solved\" means, and ensure the task is solvable by an AI agent<\/li>\n <li>Write tests that verify agent solutions - accept all valid approaches and reject incorrect ones, neither too strict nor too lenient<\/li>\n <li>Iterate on tasks and tests based on QA feedback - review agent solutions, analyze failures, and refine until the evaluation is fair and robust<\/li>\n<\/ul>\n<h4>What this is not:<\/h4>\n<ul>\n <li>Not data labeling<\/li>\n <li>Not prompt engineering<\/li>\n <li>Not writing code from scratch - the agent writes most of the code; you guide and evaluate<\/li>\n<\/ul>\n<h4>What we look for:<\/h4>\n<ul>\n <li>8+ years in software development<\/li>\n <li>Core stack: Python (FastAPI), JavaScript\/TypeScript (React), Docker, Postgres, Kafka, Redis<\/li>\n <li>Experience writing tests (functional, integration)<\/li>\n <li>English proficiency - B2+<\/li>\n<\/ul>\n<h4>Why this is hard:<\/h4>\n<p>Frontier models are already good at coding. Creating a task that genuinely challenges the best models is non-trivial. You need to deeply understand where models fail and what scenarios reveal the difference between a good and a bad solution. Tasks have many valid solutions - writing tests that accept all correct solutions and reject incorrect ones is harder than it sounds.<\/p>\n<h4>How it works<\/h4>\n<p>Apply? Pass qualification(s)? Join a project? Complete tasks? Get paid.<\/p>\n<h4>Effort estimate<\/h4>\n<p>Tasks for this project are estimated to take 30 hours to complete, depending on complexity. This is an estimate and not a schedule requirement; you choose when and how to work. Tasks must be submitted by the deadline and meet the listed acceptance criteria to be accepted.<\/p>\n<h4>Compensation:<\/h4>\n<p>Up to $150\/hr equivalent, depending on level and pace. Tasks are estimated at approximately 30 hours each; you set your own schedule.<\/p><\/p><p><\/p>

<p>Build production AI systems for clients. You'll design and ship agentic workflows on Claude and AWS Bedrock AgentCore, retrieval systems backed by Bedrock Knowledge Bases, and MCP-based automations that connect directly into client tool stacks (Notion, Linear, HubSpot, AWS, Google Workspace, and more). This is a hands-on build role. You will write code, deploy infrastructure, and sit in client rooms explaining why the system behaves the way it does.</p><p>Tasks &amp; Responsibilities</p><ul><li>Design and build applied AI systems on Claude and AWS Bedrock AgentCore: RAG pipelines backed by Bedrock Knowledge Bases, agentic workflows, and multi-tool orchestration via MCP.</li><li>Own delivery of AI components end-to-end: architecture, implementation, evaluation, and post-launch monitoring.</li><li>Integrate Claude-based agents with client infrastructure and business tools via MCP servers (HubSpot, Notion, Linear, Google Workspace).</li><li>Translate ambiguous client problems into scoped technical builds with clear success criteria and numbers attached.</li><li>Document architecture and decisions in the client's Notion record so infra details never live only in someone's head.</li><li>Work alongside Pre-Sales Engineers during scoping calls to validate technical feasibility before commitments are made.</li><li>Select and tune the right model, retrieval strategy, and Knowledge Base configuration per use case.</li><li>Build and maintain eval suites (offline test sets, regression checks, LLM-as-judge scoring) that catch quality drift before a client does.</li><li>Design agentic control flow: tool-selection logic, retry/fallback paths, and guardrails against runaway loops or unsafe tool calls.</li><li>Instrument production systems with logging, tracing, and cost/latency dashboards to debug a bad output from a client.</li><li>Write modular infrastructure as code (Terraform) for AI workloads on AWS.</li><li>Conduct architecture reviews on other engineers' AI builds and flag failure modes before they ship.</li></ul><p>Competencies</p><ul><li>Can explain to a client, in plain language, why an agent made the tool call it made</li><li>Comfortable debugging a production LLM failure under time pressure, not just in a notebook</li><li>Can wire an MCP server into an agent and debug why a tool call didn't fire</li></ul><p>Tools &amp; Certifications</p><ul><li>Strong Python engineering; comfort with AWS</li><li>Practical experience with LLM application patterns: RAG, agentic tool-use via MCP, prompt engineering, evaluation design</li><li>Hands-on experience with AWS Bedrock AgentCore and Bedrock Knowledge Bases</li><li>Nice to have: AWS Certified Machine Learning Engineer Associate, AWS Certified Generative AI Developer Professional, AWS Certified Solutions Architect Associate or Professional, AWS Certified Developer Associate, AWS Certified Security Specialty (relevant given our compliance posture)</li></ul><p><strong>Desired Candidate Profile</strong></p><p>Master's degree in Computer Science or equivalent</p><p>Previous experience building and shipping production ML/AI systems</p><p>Fluent in English and Arabic</p>

<p>Build production AI systems for clients. You'll design and ship agentic workflows on Claude and AWS Bedrock AgentCore, retrieval systems backed by Bedrock Knowledge Bases, and MCP-based automations that connect directly into client tool stacks (Notion, Linear, HubSpot, AWS, Google Workspace, and more). This is a hands-on build role. You will write code, deploy infrastructure, and sit in client rooms explaining why the system behaves the way it does.</p><p><strong>Tasks &amp; Responsibilities</strong></p><ul><li>Design and build applied AI systems on Claude and AWS Bedrock AgentCore: RAG pipelines backed by Bedrock Knowledge Bases, agentic workflows, and multi-tool orchestration via MCP.</li><li>Own delivery of AI components end-to-end: architecture, implementation, evaluation, and post-launch monitoring.</li><li>Integrate Claude-based agents with client infrastructure and business tools via MCP servers (HubSpot, Notion, Linear, Google Workspace).</li><li>Translate ambiguous client problems into scoped technical builds with clear success criteria and numbers attached.</li><li>Document architecture and decisions in the client's Notion record so infra details never live only in someone's head.</li><li>Work alongside Pre-Sales Engineers during scoping calls to validate technical feasibility before commitments are made.</li><li>Select and tune the right model, retrieval strategy, and Knowledge Base configuration per use case.</li><li>Build and maintain eval suites (offline test sets, regression checks, LLM-as-judge scoring) that catch quality drift before a client does.</li><li>Design agentic control flow: tool-selection logic, retry/fallback paths, and guardrails against runaway loops or unsafe tool calls.</li><li>Instrument production systems with logging, tracing, and cost/latency dashboards to debug a bad output from a client.</li><li>Write modular infrastructure as code (Terraform) for AI workloads on AWS.</li><li>Conduct architecture reviews on other engineers' AI builds and flag failure modes before they ship.</li></ul><p><strong>Competencies</strong></p><ul><li>Can explain to a client, in plain language, why an agent made the tool call it made</li><li>Comfortable debugging a production LLM failure under time pressure, not just in a notebook</li><li>Can wire an MCP server into an agent and debug why a tool call didn't fire</li></ul><p><strong>Desired Candidate Profile</strong></p><h2>Education, Experience &amp; Language</h2><ul><li>Master's degree in Computer Science or equivalent</li><li>Previous experience building and shipping production ML/AI systems</li><li>Fluent in English and Arabic</li></ul><h2>Tools &amp; Certifications</h2><ul><li>Strong Python engineering; comfort with AWS</li><li>Practical experience with LLM application patterns: RAG, agentic tool-use via MCP, prompt engineering, evaluation design</li><li>Hands-on experience with AWS Bedrock AgentCore and Bedrock Knowledge Bases</li><li>Nice to have: AWS Certified Machine Learning Engineer Associate, AWS Certified Generative AI Developer Professional, AWS Certified Solutions Architect Associate or Professional, AWS Certified Developer Associate, AWS Certified Security Specialty (relevant given our compliance posture)</li></ul>

<h2 class="h5">Job description</h2> <div class="t-break" data-jb-field="description"> <span>Aspire Software is looking for an Intermediate Software Developer to join our team in Lebanon.<br> Here is a little window into our company: Aspire Software operates and manages wholly owned software companies, providing mission-critical solutions across multiple verticals.<br> By implementing industry best practices, Aspire delivers a time sensitive integration process, and the operation of a decentralized model has allowed it to become a hub for creating rapid growth by reinvesting in its portfolio.<br> Role Overview We're looking for a Full Stack Developer who's equally comfortable maintaining and extending legacy .<br>NET applications as they are building modern React frontends and cloud-native services.<br> You'll work across the full stack from SQL Server stored procedures to PostgreSQL schemas to React components and use AI tools as a natural part of how you work every day.<br> This is a hands-on engineering role with real ownership.<br> You'll collaborate closely with the product and QA teams, contribute to architecture decisions, and help the team navigate the ongoing transition from legacy systems to modern infrastructure.<br> What You'll Do Maintain, debug, and extend legacy .<br>NET Framework applications and their SQL Server databases Build new features in our modern stack: React frontends backed by .<br>NET Core or Node APIs with PostgreSQL Design and optimize SQL Server and PostgreSQL schemas, queries, and stored procedures Use AI coding tools (GitHub Copilot, Claude, Cursor, or similar) to accelerate development and code review Write clean, testable code and participate in code reviews Work with QA to ensure quality across both legacy and modern surfaces Document technical decisions and contribute to internal knowledge sharing - Solid experience with nodeJS and typescript/javascript + AI - Strong SQL Server skills: schema design, stored procedures, query optimization, and migrations - Comfortable reading, maintaining, and refactoring older codebases without breaking things Modern Stack - Production experience with React (hooks, component architecture, state management) - Familiarity with .<br>NET Core / .<br>NET 6+ or equivalent modern backend frameworks - PostgreSQL experience: schema design, indexing, query tuning, and ORM usage (Entity Framework or similar) - Understanding of REST API design and integration patterns AI-Assisted Development - Active, daily use of AI coding assistants (GitHub Copilot, Claude, Cursor, or similar) - Comfortable prompting AI tools effectively for code generation, debugging, refactoring, and documentation - Able to critically evaluate AI-generated code knowing when to trust it, when to fix it, and when to throw it out - Uses AI to move faster without sacrificing quality or understanding General - Strong problem-solving instincts and attention to detail - Good communication you can explain technical trade-offs to non-technical stakeholders - Comfortable working in environments where legacy and modern systems coexist Nice to Have - Experience with cloud platforms (Azure, AWS, or GCP) - Familiarity with DevOps practices: CI/CD pipelines, containerization (Docker), infrastructure as code - Background in regulated or government-adjacent industries - Experience with TypeScript</span> </div>