Title: Machine Learning Engineer - Backend Pipelines
& Infrastructure Scaling

Job Description:
We are seeking a skilled and motivated Machine
Learning Engineer to join our team and lead the
development of backend pipelines for our expanding
machine learning models and services. The successful
candidate will have experience building scalable
infrastructure, working with popular machine learning
libraries such as PyTorch, SKlearn, Huggingface, and SentenceTransformers, and deploying machine learning models using serving solutions like TorchServe and containerization technologies such as Docker and Kubernetes.

Responsibilities:
1. Collaborate with data scientists to design,
develop, and optimize backend pipelines for machine
learning models and services.
2. Implement model training and inference pipelines
that can handle large-scale datasets using popular
machine learning libraries like PyTorch, Huggingface, SentenceTransformers, NLTK,
or Scikit-Learn.
3. Design, develop, and maintain distributed training
and inference systems to ensure fast turnaround times
and high availability.
4. Develop containerized machine learning applications
using technologies like Docker and Kubernetes,
ensuring easy deployment and scaling.
5. Implement serving solutions such as TorchServe for
model inference at scale and low latency.
6. Collaborate with the DevOps team to ensure seamless
integration of machine learning pipelines into the
production environment.
7. Develop and maintain monitoring and alerting
systems for machine learning services and
infrastructure.
8. Continuously research and evaluate new machine
learning libraries, techniques, and tools to improve
our models and services.

Requirements:
1. Bachelor's or Master's degree in Computer Science,
Engineering, Mathematics, or a related field.
2. Proven experience designing, developing, and
deploying machine learning pipelines using popular
libraries like TensorFlow, PyTorch, or Scikit-Learn.
3. Strong understanding of distributed training and
inference systems such as TensorFlow Serving,
TorchServe, and other ML serving solutions.
4. Experience with containerization technologies
(Docker) and container orchestration tools
(Kubernetes).
5. Proficiency in a scripting language like Python, R,
or Julia for data analysis and manipulation.
6. Strong problem-solving skills and the ability to
design large-scale, distributed systems.
7. Excellent written and verbal communication skills,
with the ability to work collaboratively in a team
environment.
8. Familiarity with cloud computing platforms (AWS,
Google Cloud Platform, or Azure) is a plus.
9. Experience working on data processing pipelines
using tools like Apache Spark or Hadoop is a plus.

If you are passionate about machine learning and have
the skills we're looking for, or are willing to learn, please apply today to
join our team and help us build the next generation of
AI solutions!