AI Center Technical Manager - Machine Learning Operations and Production Deployment

Description
The Role
We are seeking a highly skilled and experienced Hands-On Technical Lead to join our AI Center. The ideal candidate is a Data Scientist with a proven track record of deploying real-world AI applications at scale in large organizations. This role requires an individual who is deeply passionate about the operationalization of AI models within both data and technical ecosystems and has strong leadership skills to oversee and mentor a team of ML engineers. The primary focus will be on setting the framework to implement AI and support a path to production, working collaboratively with other GM teams to transition their ideas from PoCs to real-world applications.
This role will manager a team of employees who design, build and implement end-to-end machine learning pipelines accounting for the variability in data sources and collection policies, data analysis and feature extraction methodologies, modeling frameworks and serving infrastructure. An ML engineer exists at the intersection between science, development, and compute infrastructure.
What You'll Do

• Framework Development: Design and develop and frameworks and methodologies that enable GM teams to follow best practices in AI model deployment and operations.
• Collaborative Implementation: Work closely with Data Scientists across GM to help them transition their ideas from PoCs to production-ready AI solutions.
• Lead the Deployment of AI/ML Solutions: Oversee the end-to-end deployment of ML, custom AI, and Generative AI models, ensuring seamless integration and flawless operation within our data and technical ecosystems.
• Team Leadership: Lead, mentor, and manage a team of ML engineers, fostering a collaborative and innovative environment. Provide guidance and support to team members to ensure project success and professional growth.
• Azure and Databricks Expertise: Utilize Azure for storage and Databricks for compute, ensuring efficient and scalable data processing and model training.
• ML Operations with ML Flow: Implement and manage ML Flow as the core of our ML operations, maintaining high standards for model tracking, reproducibility, and deployment.
• Code Quality and Compliance: Establish DevSecOps pipelines with GitHub and Databricks Asset Bundles ensuring rigorous code quality, and registering ethics and bias artifacts to comply with GM's AI policy.
• Hands-On Coding: Write high-quality code, contributing directly to the development and deployment of AI models using Python.
• Code Scanning and Quality Assurance: Utilize code scanning tools like SonarQube to ensure code quality and adherence to best practices.
• End-to-End Timing Charts: Create and manage end-to-end timing charts to monitor and optimize the performance of AI models.
• Solution Design: Design solutions with an emphasis on overall maintainability and scalability.
• AI Thought Leadership: Stay conversant with current AI topics, trends, and advancements, bringing innovative ideas to the AI Center; evangelize MLOps methodology and best practices to the enterprise; attend and present work at major AI conferences
• Cross-Functional Collaboration: Work closely with data engineers, software developers, applied data scientists and business stakeholders to translate business needs into technical solutions.

Required Qualifications)

• Experience: Minimum of 10 years of experience in data science, with a strong emphasis on deploying AI applications at scale in large organizations.
• Technical Skills:
  • Expert-level Python programming
  • Extensive experience in building a range of AI/ML models from concept to production
  • Extensive experience with Azure and Databricks
  • Deep understanding of ML Flow for managing ML operations.
  • Strong knowledge of CI/CD practices and tools, particularly GitHub.
  • Ability to create and manage end-to-end model monitoring pipelines
• Operational Excellence: Demonstrated obsession with the operationalization of AI models, ensuring flawless execution and integration.
• Ethics and Compliance: Experience in registering and managing code quality, ethics, and bias artifacts. Understanding of ethical and governance challenges to deploying enterprise ML products
• Leadership Skills: Proven experience in leading and mentoring a team of engineers, with strong interpersonal and communication skills.
• Soft Skills: Excellent problem-solving skills, effective communication abilities, and a collaborative mindset.

Preferred Qualifications)

• Educational Background: Advanced degree in Computer Science, Data Science, Machine Learning, or other highly quantitative field.

Additional Description
This role is based remotely but if you live within a 50-mile radius of [Atlanta, Austin, Detroit, Warren, Milford or Mountain View], you are expected to report to that location three times a week, at minimum.
This job is not eligible for relocation benefits. Any relocation costs would be the responsibility of the selected candidate.
A company vehicle will be provided for this role with successful completion of a Motor Vehicle Report review.
Bonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance.
Benefits: GM offers a variety of health and wellbeing benefit programs. Benefit options include medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation & holidays, tuition assistance programs, employee assistance program, GM vehicle discounts and more.
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Diversity Information
General Motors is committed to being a workplace that is not only free of unlawful discrimination, but one that genuinely fosters inclusion and belonging. We strongly believe that workforce diversity creates an environment in which our employees can thrive and develop better products for our customers. We encourage interested candidates to review the key responsibilities and qualifications for each role and apply for any positions that match their skills and capabilities. Applicants in the recruitment process may be required, where applicable, to successfully complete a role-related assessment(s) and/or a pre-employment screening prior to beginning employment. To learn more, visit How we Hire
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