Jobs → Engineering → MLOps Learning Engineer

About the Role We are looking for an experienced MLOps Engineer to build, automate, and manage production-grade ML and GenAI infrastructure using AWS. Key Responsibilities • Design and manage end-to-end ML pipelines using AWS SageMaker. • Handle model training, evaluation, deployment, versioning, and monitoring. • Manage Amazon Bedrock deployments, LLM evaluations, prompt versioning, and Guardrails. • Implement automated model retraining, deployment, rollback, and CI/CD workflows. • Monitor model performance, data drift, latency, accuracy, and inference costs. • Build and optimize Docker containers and manage AWS services such as S3, ECR, Glue, and CloudWatch. • Ensure scalability, security, reliability, and reproducibility of ML systems. Requirements • 12+ years of overall Software/Data Engineering experience. • Minimum 5 years of hands-on MLOps or production ML experience. • Strong expertise in AWS SageMaker (Pipelines, Model Registry, Endpoints, Model Monitor). • Production experience with Amazon Bedrock and GenAI/LLM operations. • Strong Python, Boto3, and SageMaker SDK skills. • Hands-on experience with Docker, ECR, AWS Glue, S3, and CloudWatch. • Experience with ML CI/CD pipelines and Infrastructure as Code (AWS CDK/Terraform).