MLOps Engineer

We are looking for an experienced MLOps Engineer to build and maintain a secure, scalable deployment platform for our PDx Lead Scoring Model. The role involves automating ML deployment pipelines, implementing governance and monitoring practices, and ensuring reliable production operations.

Responsibilities:

• Build and maintain CI/CD pipelines for Machine Learning models.
• Develop and automate ML training and inference pipelines.
• Manage model deployment, versioning, registration, and lifecycle tracking.
• Implement model monitoring and observability using AWS services.
• Ensure secure ML deployments through access controls and governance.
• Troubleshoot production issues and ensure platform reliability.
• Collaborate with Data Scientists and Engineering teams to deploy ML models into production.

Requirements:

• 4+ years of experience in MLOps, DevOps, or ML Engineering.
• Proven hands-on experience deploying and supporting AI/ML models in production.
• Strong understanding of production-grade ML deployment practices.
• Ability to troubleshoot issues and manage end-to-end deployment workflows.
• Experience working with Data Science and Engineering teams in an enterprise environment.
• Preference: Candidates with strong hands-on experience in AWS SageMaker, production MLOps, and ML CI/CD automation will be preferred.

Must-Have Technical Skills:

• AWS SageMaker and AWS ML Ecosystem
• MLOps / Production ML Model Deployment
• CI/CD Pipelines for ML Models
• Automated ML Training and Inference Pipelines
• Python and Git
• Docker and Containerization
• Model Registry, Versioning, and Experiment Tracking
• AWS CloudWatch and Monitoring
• Security, Access Management, and Model Governance

Skills

AWS, CI / CD, DevOps, Docker, MLOps, Python, SageMaker