About the role
Lead the design, build, and maintenance of end-to-end MLOps and LLMOps pipelines. Implement CI/CD workflows, containerize ML workloads, and architect solutions on AWS. Develop monitoring, automate model versioning, and ensure security and compliance. Mentor junior engineers and drive technical roadmaps.
BankingOnsite
Key Responsibilities
- Lead design, build, and maintenance of end-to-end MLOps and LLMOps pipelines.
- Implement CI/CD workflows using Bitbucket, Jenkins, and automation tools.
- Containerize ML workloads with Docker and orchestrate on Kubernetes (EKS).
- Architect and implement AWS solutions for model training and inference.
- Develop monitoring and alerting for model performance and infrastructure health.
- Oversee automation of model versioning, artifact storage, and metadata tracking.
Requirements
- Degree in Computer Science, Software Engineering, Data Engineering, or related field.
- 5+ years of experience in MLOps, DevOps, or cloud-native engineering.
- 2+ years in a leadership role.
- Proficiency in Python and model inferencing stack (Ray, VLLM, SGLang).
- Hands-on experience with Docker and Kubernetes/EKS.
- Expert knowledge of AWS cloud services (SageMaker, ECR, EKS, Lambda, etc.).