Description
Aspire Software is looking for an AI Automation & Agent Builder to join our team in Lebanon.
Here is a little window into our company: 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.
About the AI-Driven Automation
The AI Automation & Agent Builder Engineer is an AI-first builder responsible for designing, building, and shipping intelligent agents and automated workflows that replace manual work and generate measurable business value. This role is not a research or experimentation role. AI Automation Engineers are embedded directly inside portfolio company teams, understanding real operations, and deploying agents that handle real work — customer service, sales outreach, onboarding, document processing, and more.
Some of what you build will replace manual workflows within Aspire’s portfolio companies. Some of it will become new revenue-generating products sold to their customer bases. Either way, it ships to real users and is measured by real impact. This role is foundational in AI-native organizations and is often the highest-ROI AI position in the company.
Core Responsibilities
Build Internal AI Agent Teams
Design and deploy AI agents that handle operational work across portfolio companies — including customer support (Tier 1 & 2), SDR and sales outreach, onboarding automation, content workflows, and engineering maintenance
Automate core business processes: CRM updates, document processing, reporting, and analytics pipelines
Work directly with portfolio company operators to identify the highest-value automation opportunities
Measure impact in hours of labor replaced, team output increased, or cost savings generated
Build External AI Products
Develop new AI-powered products such as vertical voice agents, AI copilots, workflow automation tools, and AI documentation systems tailored to vertical markets
Own the product from prototype through launch, including reaching first paying customers
Integrate AI capabilities with existing systems: CRM, ERP, internal tools, SaaS platforms, and third-party APIs
Build agentic workflows including multi-agent coordination, decision engines, and tool-using AI agents
Ship and Iterate
Move fast from idea to MVP to production — bias toward working software over documentation
Run beta tests with real users, gather feedback, and improve continuously
Optimize AI agent performance: latency, cost, accuracy, and human intervention rate
Ensure production reliability through monitoring, evaluation, and model lifecycle management
Collaborate cross-functionally with product, engineering, and go-to-market teams
What This Role Is Not
Not a research or proof-of-concept role — everything you build ships to real users
Not a traditional backend or systems engineering role focused on ticket execution
Not bound by story points, sprint rituals, or rigid SDLC stages
Not limited to internal tooling — what you build may become a product customers pay for
Requirements
Core Capabilities
Proven track record of building products, tools, automations, or side projects that went live and created real impact
Strong working knowledge of AI and machine learning tools, large language models (LLMs), and agent frameworks
Ability to go from zero to functional prototype quickly
Comfort with ambiguity — you will often be defining the problem as much as the solution
Bias toward action over planning
Technical Skills
Hands-on experience with LLM integrations, RAG architecture, and prompt engineering
Proficiency with workflow orchestration and automation platforms (e.g. n8n, Zapier, Make, LangChain, CrewAI)
Ability to build and maintain API integrations across CRM, ERP, and SaaS systems
Understanding of vector databases, embedding pipelines, and model hosting
Strong debugging and problem-solving skills across full AI system stacks
Mindset & Approach
Outcome-oriented: you measure success in labor hours saved, revenue generated, and adoption rate — not tickets closed
Strong operational intuition — ability to understand a business workflow and identify where AI creates the most leverage
Comfortable working directly with business operators, not just engineering teams
Ability to reason about AI system limitations, failure modes, and appropriate human-in-the-loop design
Key Performance Indicators (KPIs)
Automation Impact
Number of business processes automated
Hours of manual labor saved per month
Reduction in manual task volume across portfolio companies
Agent Performance
Agent task success rate
Agent accuracy and hallucination rate
Human intervention rate (lower = better)
Efficiency & Business Value
Cost savings generated through automation
Response time improvements across automated workflows
Operational throughput increase
Delivery & Adoption
Number of AI agents and automations shipped per quarter
Time from idea to production deployment
Number of teams actively using AI agents built by this role
Internal workflow adoption rate