Position- Machine Learning Engineer
Experience - 3 to 5 years
Job Location - Pune / Remote
Immediate Joiner Only
Role Overview
The ML Ops Engineer plays a critical role in the development, automation, and deployment of Machine Learning (ML) and Generative AI (GenAI) pipelines across AWS cloud environments. This hands-on role emphasizes building reproducible workflows, integrating observability tools, and enabling efficient, scalable model delivery. The position supports AI systems deployed in banking environments, where resilience and reliability are paramount.
Key Experience:
- 3-5 years Strong programming skills in Python, with experience in pandas, SQL, and ML frameworks (e.g., scikit-learn).
- Should have used Python in ML domain to build, train and maintain models.
- Familiarity with AWS services such as Lambda, Glue, CloudWatch, and Bedrock.
- Experience with container workflows (Docker) and model lifecycle management.
- Foundational knowledge of observability practices and model deployment fundamentals.
- Interest or experience in supporting AI systems used by developers or analysts.
- Strong communication and documentation skills with a collaborative team mindset.
- Ability to assume ownership of assignments and consistently meet deadlines.
Responsibilities:
- Deploy and maintain ML and GenAI models using AWS services, including SageMaker, Fargate, and Bedrock.
- Apply prompt engineering techniques to optimize GenAI model performance and reliability.
- Experience with Retrieval-Augmented Generation (RAG) applications is a plus.
- Assist in building and maintaining internal model-serving platforms to support development teams.
- Implement containerized services using Docker and deploy them to AWS infrastructure.
- Write Infrastructure-as-Code (IaC) using Terraform to automate cloud resource provisioning (nice to have).
- Participate in unit and end-to-end testing of ML pipelines, services, and monitoring workflows.
- Support model monitoring and health tracking using AWS CloudWatch and internal observability tools.
- Document internal systems and operational processes to ensure maintainability and reproducibility.
