Role overview
- Hands-on experience with Amazon SageMaker
- CI/CD setup for ML models using AWS CodePipeline & CodeBuild
- Strong experience with Docker and container orchestration (ECS/Fargate)
- ML model deployment, monitoring, and lifecycle management
- Data pipeline development using Python, PySpark, and Snowflake
- Proficiency in SQL and Bash/Shell scripting
- Experience with ML libraries: Pandas, NumPy, PyTorch, Scikit-learn
- Strong knowledge of AWS services: S3, IAM, Lambda, Step Functions, ECR
- Data and model quality monitoring experience
What you'll work on
- Build and automate end-to-end ML pipelines
- Deploy and manage ML models in production
- Monitor model performance and data quality
- Maintain CI/CD workflows and infrastructure
- Ensure security, compliance, and system reliability
- Continuously optimize performance and cost
Tags & focus areas
Used for matching and alerts on DevFound Fulltime Machine Learning Mlops Pytorch Ai