Description

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

This role is on-site.

Responsibilities

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

Required Skills

• 8+ years in software or 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 or TensorFlow.
• Experience with AWS services including SageMaker, Bedrock, Lambda, EKS/ECS, S3, Step Functions, API Gateway, CloudWatch, and IAM.
• Experience with containerization using Docker and Kubernetes.
• Familiarity with Terraform or CloudFormation and Jenkins or GitHub Actions for CI/CD.
• Knowledge of model governance, access control, data security, and compliance practices.

Preferred Skills

• Bachelor's degree in Computer Science or related field.
• 3+ years tenure in previous relevant roles.

Key Skills

Education

Bachelor's degree