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Lead MLOps Architect

2 months ago

Houston, Texas, United States MD Anderson Center Full time


MD Anderson is enhancing its Enterprise MLOps and Analytics Platform capabilities to provide improved support for MLOps and ModelOps, facilitating the operationalization, oversight, and management of both internal and external AI solutions.

This enhancement is crucial for reinforcing our comprehensive AI Governance framework.


We are currently in search of a Lead MLOps Architect to spearhead and build a team of MLOps engineers responsible for designing and advancing our Enterprise MLOps and Analytics Platform.

The Lead MLOps Architect will play a critical role in managing operations and supervising the entire MLOps and ModelOps technology stack, ensuring effective end-to-end lifecycle management and governance of machine learning models across the organization.

As the primary authority in MLOps, this position entails defining team dynamics, cultivating a positive culture, and implementing processes and technologies vital for supporting ML solutions within our hybrid data and computational framework.

Working closely with IT, cybersecurity, and compliance teams, the Lead MLOps Architect will be key in establishing a secure and compliant infrastructure for the scalable management of AI/ML models.


Key Responsibilities Include:

  • Mentor and lead a team of MLOps Engineers to develop a scalable MLOps and Analytics platform within a hybrid compute environment, utilizing technologies such as Kubernetes and Azure.
  • Design, implement, and manage CI/CD pipelines, ensuring the infrastructure supports ML model training, deployment, and monitoring while maintaining security, scalability, reliability, and performance.
  • Develop, refine, and standardize Model Governance integrations, performance tracking for bias and impact, and a model catalog with standardized scorecards and deployments.
  • Innovate automated validation, deployment, observability, and management tools for scalable and reproducible AI solutions.
  • Formulate fallback and decommissioning strategies for AI solutions to ensure operational continuity.
  • Provide training on AI solutions to enhance understanding and application throughout the organization.
  • Engage with technology trends, contribute to tech communities, and promote a culture of continuous learning and innovation.
Technical Expertise:
Demonstrate a profound understanding of AI/ML Platform infrastructure and cloud architecture.
Experience in developing and deploying AI/ML algorithms into production.

Strong proficiency in Python and C++ or C#, along with experience in machine learning libraries such as TensorFlow, PyTorch, and Scikit-learn.

Familiarity with DevOps practices, CI/CD pipelines, including tools like Azure DevOps or Git Actions.
Proficiency in working with containers such as Docker and container orchestration systems like Kubernetes. Knowledge of process orchestration/DAGs tools.
Experience with AI/ML algorithms and packages (e.g., scikit, PyTorch, TensorFlow).
Experience with data, code, and model artifact management processes and MLOps tools.
Experience with on-premises, cloud-based, and hybrid computing environments, as well as cloud-native tools and services.
Understanding of ISO standards for software and/or AI development lifecycle management.

Analytical Skills:
Experience with project management methodologies (e.g., SAFe agile, PRINCE2, Lean methodology).
Deep understanding of the AI/ML Model Lifecycle Management.
Proficient in decision-making, problem-solving, and successfully executing AI/ML solutions in a healthcare context.
Manage AI/ML projects throughout their lifecycle, ensuring timely delivery, budget adherence, and compliance with quality standards.
Experience leading an ML engineering and/or data science team focused on developing, deploying, and maintaining production-ready models.
Experience collaborating with third-party vendors and partners to integrate new AI solutions into existing infrastructure and workflows.
Experience implementing risk identification and mitigation strategies, including contingency planning, to avert project delays and complications.
Preference for familiarity with hospital workflows.
Preference for experience with healthcare data privacy and security protocols, such as HIPAA.
Preference for experience with flowcharts, business process models, mapping tools, data flow diagrams, and process flow diagrams.

Professionalism:
Oral and Written


Collaborate closely with data scientists, ML engineers, software engineers, and other stakeholders to understand requirements and integrate machine learning models into the overall system.

Create and maintain comprehensive documentation for CI/CD pipelines, deployment processes, and infrastructure configurations.

Experience reporting on project progress, impact, and risks to leadership and stakeholders, providing strategic advice to help prioritize AI/ML solutions use-cases.

Experience in stakeholder management to drive adoption, address concerns, and prioritize solution support.

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

Preferred Education:
Master's Level Degree

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

Preferred Experience:
Experience working on production-quality healthcare-focused machine learning solutions.

Proficiency in architecting, implementing, and utilizing MLOps solutions.

Onsite Presence:
Is Not Required


It is the policy of The University of Texas MD Anderson Cancer Center to provide equal employment opportunity 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.