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

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