Mlops Engineer

ML DevOps Engineer / MLOps Engineer

Location: Hartford, CT (Onsite)Employment: Fulltime

Must‑Have Technical & Functional Skills

1. AWS SageMaker Experience
2. • Hands-on work with model training, tuning, deployment
   • Experience setting up CI/CD pipelines using AWS CodePipeline/CodeBuild for ML models (cloud preferred)
3. Containerization & Deployment
4. • Docker
   • Container orchestration (ECS, Fargate, EKS preferred)
   • ML model deployment, monitoring, and lifecycle maintenance
5. Data Pipeline Development
6. • Design, build, and scale reliable data pipelines using AWS + Python+ PySpark + Snowflake
7. Programming & ML Experience
8. • Bash/Shell scripting
   • SQL
   • ML libraries: Pandas, NumPy, PyTorch, Scikit-learn
9. Data & Model Quality Monitoring
10. • Drift detection
    • Validation checks
    • Logging & observability best practices
11. AWS Core Services
12. • 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

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