Senior AI Model Engineer

2 weeks ago

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

About Us
The University of Texas M. D. Anderson Cancer Center is dedicated to the mission of eradicating cancer through exceptional programs that merge patient care, research, prevention, and education. A key component of our success lies in our ability to manage complex data, conduct data analytics, and implement machine learning solutions that create a lasting impact within a framework of responsible artificial intelligence.

Position Overview
We are in search of a Senior MLOps Engineer who will be instrumental in enhancing MLOps initiatives throughout the organization. This role is essential for establishing an AI lifecycle management framework that includes the development, deployment, and upkeep of high-quality 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, contributing to a culture of innovation and collaboration.

Key Responsibilities
  1. Manage the complete lifecycle of AI models, including training, evaluation, deployment, monitoring, and maintenance of production-grade 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. Implement rigorous testing, versioning, and documentation processes to ensure impact, risk management, and reproducibility.
  4. Promote a culture of responsible AI by reducing bias, enhancing fairness, and maximizing transparency in AI models.
  5. Maintain comprehensive records of model development experiments, data and model lineage tracking, as well as data and model scorecards.
  6. Collaborate with stakeholders to gather requirements, communicate AI concepts clearly, and collect feedback.
  7. Design strategies for fallback and decommissioning of AI solutions to ensure operational continuity.
  8. Assist in the evaluation and integration of third-party machine learning models, ensuring compliance with institutional standards and minimizing organizational risk.
  9. Provide training on AI solutions to enhance understanding and application across the organization.
  10. Stay updated with technology trends, contribute to tech communities, and foster a culture of continuous learning and innovation.
Technical Expertise
  1. Expertise in developing, deploying, and maintaining AI/ML algorithms in production settings.
  2. Proficient in building scalable data pipelines, feature management, and analytics.
  3. Experienced with MLOps tools and processes for managing data, code, and models.
  4. Strong skills 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 solutions.
  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. Skilled 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. Proficient in decision-making, problem-solving, and executing AI/ML healthcare solutions.
  4. Adept at quantitatively assessing machine learning models for performance, workflow impact, and potential risks.
  5. Capable of collaborating with vendors and partners for evaluating and integrating third-party AI solutions into existing systems and processes.
  6. Competent in identifying risks and developing mitigation strategies to prevent project delays.
Communication Skills
  1. Work closely with data scientists, ML engineers, and software engineers to integrate machine learning models into existing 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 relations to facilitate solution adoption and address challenges.
  5. Share knowledge and provide technical assistance 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: Bachelor's degree in Computer Science, Software Engineering, Data Science, Physics, Math & Statistics, or a related engineering discipline.

Preferred: Master's Level Degree.

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

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