onsite
Senior Machine Learning Operations Engineer - MD Anderson Cancer Center
ML Engineer
Lead enterprise‑wide AI initiatives in healthcare, designing and deploying robust ML pipelines on AWS, leveraging Docker/Kubernetes for scalable MLOps, and ensuring data governance and model reliability to improve cancer care outcomes.
About the role
Key Responsibilities
- Design, develop, and maintain production‑grade machine learning pipelines using Python and AWS services (SageMaker, Lambda, ECS).
- Implement MLOps practices: CI/CD, model versioning, monitoring, and automated retraining with Docker and Kubernetes.
- Collaborate with data scientists, clinicians, and data engineers to integrate multidimensional healthcare data, ensuring compliance with privacy and governance standards.
- Optimize model performance and scalability, conduct root‑cause analysis, and provide actionable insights to improve clinical decision support.
- Document architecture, processes, and best practices for cross‑functional teams.
Requirements
- 5+ years of experience in machine learning engineering, preferably in healthcare or life sciences.
- Proficiency in Python, SQL, and cloud‑native MLOps tools (AWS, Docker, Kubernetes).
- Strong understanding of data governance, privacy regulations (HIPAA), and model interpretability.
- Excellent communication skills and ability to translate technical concepts to non‑technical stakeholders.
- Experience with CI/CD pipelines, automated testing, and monitoring frameworks.
Skills
pythonmachine learningawsmlopsdockerkubernetessql