Note: The job is a remote job and is open to candidates in USA. The University of Texas MD Anderson Cancer Center is seeking a Senior Machine Learning Operations Engineer to support enterprise-wide artificial intelligence initiatives within Data Impact & Governance. The role focuses on building, deploying, monitoring, and sustaining production-quality machine learning systems while partnering with data scientists, engineers, clinicians, and business stakeholders to ensure scalable, secure, reliable, and responsible AI solutions.
Responsibilities
- Oversee end-to-end AI model lifecycles including training, evaluation, deployment, monitoring, and maintenance of production-quality machine learning models
- Design and implement CI/CD pipelines for model training, deployment, monitoring, and retraining with a focus on security, scalability, reliability, reproducibility, and performance
- Implement rigorous testing, versioning, and documentation practices to support reproducibility, risk mitigation, and measurable impact
- Maintain comprehensive experiment tracking, data lineage, model lineage, and model scorecards
- Design fallback, rollback, and decommissioning strategies to ensure operational continuity of AI solutions
- Promote responsible AI practices by minimizing bias, enhancing fairness, and maximizing transparency in machine learning models
- Ensure AI lifecycle management aligns with institutional standards and best practices
- Support assessment, validation, and onboarding of external machine learning models and AI-driven products to minimize organizational risk and maximize value
- Develop and maintain scalable data pipelines, feature stores, and artifact management systems
- Deploy and operate ML workloads across cloud and on-premises environments including Azure, AWS, or GCP
- Utilize containerization and orchestration technologies such as Docker, Kubernetes, and DAG-based tools
- Apply DevOps and MLOps tools including Azure DevOps, GitHub Actions, and version control systems
- Collaborate with stakeholders to gather requirements, translate AI concepts into understandable terms, and incorporate feedback
- Partner with data scientists, ML engineers, and software engineers to integrate models into enterprise systems
- Deliver training and knowledge sharing to enhance AI understanding and adoption across the organization
- Report project progress, impact, risks, and recommendations to leadership
- Stay current with emerging technology trends in AI, MLOps, and healthcare analytics
- Contribute to internal and external technical communities
- Foster a culture of continuous improvement, innovation, and learning across teams
- Perform other duties as assigned
Skills
- **Work Location: Remote within Texas only.**
- **Education Required**: Bachelor's degree in Computer Science, Software Engineering, Data Science, Physics, Math & Statistics, or another related engineering discipline
- **Experience Required :** Five years of experience in machine learning engineering, data science, data engineering, and/or software engineering. With Master's degree, three years' experience required. With PhD, one year of experience required
- **Preferred Education:** Master's Level Degree
- **Preferred Experience:** Experience developing MLOps pipelines for computer vision AI models, hands on experience developing custom machine learning algorithms from scratch (e.g., in NumPy or PyTorch, designed and implemented shared machine learning service that is used across multiple teams or production projects, led the development of systems that automate the deployment and maintenance of multiple machine learning models into user-facing products, five years of industry experience in data science, with at least 3 of those years as a Senior Machine Learning Engineer
Benefits
- Employer-paid medical coverage starting day one for employees working 30+ hours/week
- Optional group dental, vision, life, AD&D, and disability insurance
- Accruals for PTO and Extended Illness Bank
- Paid holidays
- Wellness leave
- Childcare leave
- Other leave options
- Tuition Assistance Program after six months of service
- Extensive wellness and fitness resources
- Employee resource groups
- Defined-benefit pension through the Teachers Retirement System
- Voluntary retirement plans
- Employer-paid life insurance
- Employer-paid reduced salary protection programs
- Medical benefits
- Dental benefits
- Paid time off
- Retirement benefits
- Tuition benefits
- Educational opportunities
- Individual and team recognition
- Referral bonus
- Relocation assistance
Company Overview
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