Description

You build and manage end-to-end MLOps pipelines for training, deployment, monitoring, and retraining.

Responsibilities

• Deploy and optimize Generative AI/LLM applications, including RAG-based solutions.
• Productionize ML models and enable scalable inference services.
• Implement CI/CD automation for ML workflows.
• Monitor model performance, drift detection, and automated retraining.
• Implement AI/ML governance, security, compliance, and responsible AI controls.

Required Skills

• 8+ years in software/ML engineering with strong experience in cloud-based ML deployments.
• Strong experience in Generative AI, LLMs, Prompt Engineering, and RAG frameworks.
• Hands-on expertise in MLOps and ML lifecycle management.
• Proficiency in Python and PyTorch/TensorFlow.
• Experience with AWS services including SageMaker, Bedrock, Lambda, EKS/ECS, S3, Step Functions, API Gateway, CloudWatch, IAM.
• Experience with containerization using Docker and Kubernetes.
• Familiarity with Terraform/CloudFormation and Jenkins/GitHub Actions for CI/CD.
• Knowledge of model governance, access control, data security, and compliance practices.

Key Skills

Education

Bachelor's degree