Senior AI Operations Engineer

2 weeks ago


Houston, Texas, United States University of Texas MD Anderson Cancer Center Full time

Mission Statement
The University of Texas M. D. Anderson Cancer Center is dedicated to eradicating cancer through exceptional programs that merge patient care, research, prevention, and education. A vital component of our mission is the ability to manage complex data, perform data analytics, and implement machine learning to foster a sustainable impact within a framework of responsible artificial intelligence.

Position Overview
We are seeking a Senior MLOps Engineer who will be instrumental in propelling MLOps initiatives throughout the organization. This position is essential for establishing an AI lifecycle management framework that includes the development, deployment, and upkeep of production-grade machine learning models to bolster clinical and operational functions. The Senior MLOps Engineer will also evaluate and validate external machine learning models and AI-driven solutions. This role transcends technical skills; it involves nurturing team dynamics, fostering a culture of innovation, and supporting the necessary processes and technological foundations to enhance robust MLOps practices.

Key Responsibilities
  1. Manage the lifecycle of AI models, including training, evaluation, deployment, monitoring, and maintenance of production-quality machine learning models, adhering to established standards and best practices.
  2. Create CI/CD pipelines for ML model training, deployment, and monitoring while ensuring security, scalability, reliability, reproducibility, and performance.
  3. Conduct thorough testing, versioning, and documentation to ensure impact, risk mitigation, and reproducibility.
  4. Promote a culture of responsible AI by reducing bias, improving fairness, and maximizing transparency in AI models.
  5. Maintain comprehensive records of model development experiments, data and model lineage tracking, and data and model scorecards.
  6. Collaborate with stakeholders to gather requirements, clearly communicate AI concepts, and collect feedback.
  7. Design fallback and decommissioning strategies for AI solutions to guarantee operational continuity.
  8. Assist in the evaluation and integration of third-party machine learning models, ensuring they meet institutional standards and enhance institutional value.
  9. Provide training on AI solutions to improve understanding and application across the organization.
  10. Stay informed about technology trends, contribute to tech communities, and promote a culture of continuous learning and innovation.
Technical Expertise
  1. Expertise in developing, deploying, and maintaining AI/ML algorithms in production settings.
  2. Skilled in building scalable data pipelines, feature and artifact management, and analytics.
  3. Experienced with MLOps tools and processes for managing data, code, and models.
  4. Strong proficiency in Python and either C++ or C#, with practical experience in TensorFlow, PyTorch, and Scikit-learn.
  5. Knowledgeable about AI/ML platform infrastructure, including cloud and on-premises architectures.
  6. Familiar with cloud-native tools, services, and computing environments (e.g., Azure, AWS, GCP).
  7. Proficient in DevOps practices and CI/CD pipelines, including Azure DevOps and GitHub Actions.
  8. Experienced with containerization using Docker and orchestration with Kubernetes, along with DAGs tools.
Analytical Expertise
  1. Proficient in project management methodologies (SAFe agile, PRINCE2, Lean) for comprehensive AI/ML project lifecycle management, ensuring timely delivery, budget adherence, and quality compliance.
  2. In-depth understanding of AI/ML Model Lifecycle Management aligned with ISO standards for software and AI development.
  3. Skilled in decision-making, problem-solving, and executing AI/ML healthcare solutions.
  4. Adept at quantitatively evaluating machine learning models for performance, workflow impact, and potential risks.
  5. Competent in collaborating with vendors and partners for assessing and integrating third-party AI solutions into existing systems and processes.
  6. Capable of identifying risks and developing mitigation plans to prevent project delays.
Communication Skills
  1. Work collaboratively with data scientists, ML engineers, and software engineers to integrate machine learning models into current systems.
  2. Document CI/CD pipelines, deployment workflows, and infrastructure setups.
  3. Report project metrics, including progress, impact, and risks, to leadership, providing strategic recommendations for AI/ML use-case prioritization.
  4. Manage stakeholder relationships to facilitate solution adoption and address concerns.
  5. Share knowledge and provide technical support to researchers and colleagues.
  6. Deliver both technical and non-technical updates in meetings and at professional gatherings.
  7. Engage effectively with team leaders, peers, end-users, and support staff as needed.
Education and Experience

Required Education: Bachelor's degree in Computer Science, Software Engineering, Data Science, Physics, Math & Statistics, or a related engineering discipline.

Preferred Education: Master's Level Degree.

Required Experience: Five years of experience in machine learning engineering, data science, data engineering, and/or software engineering. With a Master's degree, three years of experience is required. With a PhD, one year of experience is required.

Preferred Experience: Experience in developing MLOps pipelines for computer vision AI models, leading the deployment and maintenance of multiple machine learning models in production environments, and five years of industry experience in data science, with at least three of those years as a Senior Machine Learning Engineer.

Equal Employment Opportunity: The University of Texas MD Anderson Cancer Center is committed to providing equal employment opportunities without regard to race, color, religion, age, national origin, sex, gender, sexual orientation, gender identity/expression, disability, protected veteran status, genetic information, or any other basis protected by institutional policy or by federal, state, or local laws unless such distinction is required by law.

Additional Information
  • Requisition ID: 168800
  • Employment Status: Full-Time
  • Employee Status: Regular
  • Work Week: Days
  • Minimum Salary: US Dollar (USD) 145,500
  • Midpoint Salary: US Dollar (USD) 182,000
  • Maximum Salary: US Dollar (USD) 218,500
  • FLSA: exempt and not eligible for overtime pay
  • Fund Type: Hard
  • Work Location: Remote
  • Pivotal Position: Yes
  • Referral Bonus Available?: Yes
  • Relocation Assistance Available?: Yes
  • Science Jobs: No


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