The Machine Learning Engineer – Platforms supports the development, reliability, and scalability of the enterprise AI/ ML platform used across clinical and business operations. The role focuses on ML - Ops engineering, platform integration, automation, container management, model monitoring, and lifecycle governance. The engineer partners closely with data scientists, ML engineers, and enterprise IT teams to support AI development and deployment, while ensuring compliance, performance, and responsible AI practices. Major Work Activities Technical ExpertiseSupport development, administration, and maintenance of the enterprise AI/ ML platform (Dataiku, Kubernetes, Azure), ensuring scalability, reliability, and smooth integration with institutional systems. Orchestrate training, deployment, and inference pipelines within Dataiku targeting Azure and on-premises Kubernetes clusters. Develop and maintain ML - Ops workflows for reproducibility, version control, governance, and model lifecycle management. Manage and optimize containerized environments using Docker and Kubernetes to support data science workloads. Provide platform support for data scientists and ML engineers, troubleshooting environment, pipeline, and dependency issues. Monitor platform performance, cost, security, and compliance, ensuring alignment with enterprise and regulatory standards. Analytical Skills. Build and support scalable pipelines in Dataiku, Kubernetes, and Azure, including feature engineering, model tracking, and validation workflows. Debug, test, and resolve complex platform or pipeline issues using strong analytical and problem-solving skills. Assist with healthcare data integration using standards such as HL 7, FHIR, or DICOM when required for model development. Professionalism: Oral & Written Communication. Share platform knowledge, best practices, and methodologies through training, documentation, and cross-team collaboration. Support analytics and automation workflows by enabling access to data, reviewing project requests, and assisting with interpretation. Communicate platform updates, risks, performance, and issue resolutions clearly during meetings and collaborative sessions. Work effectively with leaders, technical peers, and end users, ensuring strong communication across both technical and non-technical stakeholders. Other Duties. Perform additional tasks as assigned to support the AI/ ML platform, ML - Ops practices, and enterprise data science initiatives. EDUCATION - Required: Bachelor's Degree Computer Science, Software Engineering, Data Science, Physics, Math & Statistics, or another related engineering discipline. Preferred: Master's Degree Computer Science, Software Engineering, Data Science, Physics, Math & Statistics, or another related engineering discipline. WORK EXPERIENCE - Required: 3 years in machine learning engineering, data science, data engineering, and/or software engineering experience. Required: 1 year experience with Master's degree. No experience required with PhD. Preferred Experience/ Skills: Healthcare experience needed, experience with ML - Ops platforms and/or cloud AI certifications, strong proficiency in CI/ CD and automation of the AI lifecycle, experience working on healthcare focused machine learning projects. Experience with Azure and/or Kubernetes. Proficiency in services such as Azure Kubernetes Services and Azure ML (or similar). The University of Texas MD Anderson Cancer Center offers excellent benefits, including medical, dental, paid time off, retirement, tuition benefits, educational opportunities, and individual and team recognition. This position may be responsible for maintaining the security and integrity of critical infrastructure, as defined in Section 113.001(2) of the Texas Business and Commerce Code and therefore may require routine reviews and screening. The ability to satisfy and maintain all requirements necessary to ensure the continued security and integrity of such infrastructure is a condition of hire and continued employment.