Key Responsibilities :
- Design, build, and maintain the MLOps platform enabling scalable, reproducible, and observable ML and GenAI workflows.
- Develop and manage core ML infrastructure —feature stores, model registries, CI / CD and data pipelines—using AWS services (SageMaker, ECS / EKS, Lambda, Step Functions).
- Support AI and ML teams with tooling, best practices, and technical guidance for training, fine-tuning, and model deployment.
- Drive architecture and technology decisions across the ML stack—frameworks, orchestration, and monitoring tools.
- Collaborate with AI engineering teams to integrate LLM and Generative AI capabilities into products using standardized, secure infrastructure.
- Ensure scalability, reliability, and compliance through DevOps and Infrastructure-as-Code (IaC) best practices.
- Evaluate and adopt emerging technologies such as LangChain, Ray, MLflow, Kubeflow, Hugging Face to enhance platform efficiency.
Skills Required
Computer Science, Printing, Automation, Product Engineering, Machine Learning, Packaging