About the role
We are looking for an experienced MLOps Engineer to build and maintain a secure, scalable deployment platform for the PDx Model. The role involves automating ML release pipelines, implementing governance and monitoring, and providing production support for the PDx Lead Scoring Model.
Responsibilities:
- Build and maintain secure ML deployment platforms using AWS SageMaker and related AWS services.
- Develop automated CI/CD pipelines for ML model training, testing, and deployment.
- Automate build and release processes for training and inference pipelines.
- Implement model registry, versioning, experiment tracking, and lifecycle management.
- Use Docker for containerization and support reliable ML deployments.
- Monitor production systems using AWS CloudWatch and observability tools.
- Implement security controls, access management, governance, and model lineage.
- Troubleshoot production issues and ensure reliable operation of deployed ML models.
- Collaborate with Data Scientists and engineering teams to transition ML models into production.
- Maintain Python codebases and Git repositories while improving deployment automation.
Requirements:
- 4-7 years of engineering experience with hands-on production AI/ML deployments.
- Strong practical experience with AWS SageMaker and ML deployment pipelines.
- Proven experience in CI/CD automation and production ML operations.
- Strong troubleshooting, ownership, and problem-solving skills.
- Experience working in enterprise cloud environments.
- Excellent communication and stakeholder collaboration skills.
- Immediate joiners preferred.
Key Skills:
- AWS SageMaker and AWS ML Ecosystem
- MLOps and Machine Learning Deployment
- CI/CD Pipeline Automation
- Python and Git
- Docker and Containerization
- ML Training and Inference Pipelines
- Model Registry, Versioning and Experiment Tracking
- AWS CloudWatch and Observability
- Security, Access Control and Model Lineage
- Production Support and Troubleshooting