MLOps Engineer

ML DevOps Engineer / MLOps Engineer Location: Hartford, CT (Onsite) Employment: Fulltime Must-Have Technical & Functional Skills AWS SageMaker Experience Hands-on work with model training, tuning, deployment Experience setting up CI/CD pipelines using AWS CodePipeline/CodeBuild for ML models (cloud preferred) Containerization & Deployment Docker Container orchestration (ECS, Fargate, EKS preferred) ML model deployment, monitoring, and lifecycle maintenance Data Pipeline Development Design, build, and scale reliable data pipelines using AWS + Python + PySpark + Snowflake Programming & ML Experience Bash/Shell scripting SQL ML libraries: Pandas, NumPy, PyTorch, Scikit-learn Data & Model Quality Monitoring Drift detection Validation checks Logging & observability best practices AWS Core Services S3 IAM Lambda Step Functions CodeBuild ECR ECS / Fargate Roles & Responsibilities 1. ML Pipeline Development & Automation Build automated ML pipelines for data ingestion, training, and evaluation Implement CI/CD for ML workflows 2. Model Deployment & Serving Deploy models to real-time or batch inference endpoints Manage rollouts, scaling, versioning, and rollback strategies 3. Development & Maintenance Maintain ML services, data flows, and automation infrastructure Resolve production issues and optimize pipeline reliability 4. Monitoring & Performance Management Monitor model performance, data quality, drift, and latency Implement alerts and dashboards for pipeline health 5. Security & Compliance Implement secure access control (IAM) Ensure compliance with data governance and cloud security practices 6. Continuous Improvement Optimize cost, performance, and reliability of ML infrastructure Recommend improvements to MLOps processes and tooling

Created: 2026-03-04