Manager, AI/ML Engineering

Stellantis•Auburn Hills, MI

11h

About The Position

The Manager of AI/ML Engineering — North America leads and develops an AI/ML engineering team while remaining hands-on as a player-coach. The manager personally designs, builds, and ships production AI/ML solutions end-to-end, including data pipelines, models and inference services, agentic or LLM components, APIs, and user-facing applications or dashboards. The role owns the team’s delivery commitments and production health, takes on the hardest solutions, establishes engineering standards, and raises the technical capability of the team. Engineering owns products from design through production and ongoing maintenance and optimization. Requirements, priorities, and direction are shaped in partnership with program and product-management teams. The manager contributes directly to code and solutions, works closely with a dedicated MLOps team on release and operation, and routes production-driven code changes through the team. The role also works across a global organization, coordinating with functional leads and globally dispersed project teams and building with global platforms and standards.

Requirements

Bachelor's degree in engineering, computer science, applied mathematics, or a related field.
At least 10 years of software engineering experience, including substantial hands-on solution development involving services, APIs, and user-facing applications or front-end/full-stack work.
Experience leading or coordinating a team of engineers.
Track record of shipping and operating production software, with ownership of production systems and continued hands-on involvement.
Practical experience delivering ML/AI-powered products.
Strong Python skills and experience with common ML libraries, with ability to work across data, modeling, and software concerns.
Cloud experience and experience with modern data/ML platforms such as Databricks or Spark.

Nice To Haves

Experience building agentic/LLM-based systems and adopting AI-assisted development tools across a team.
Automotive, industrial, IoT, regulated, or embedded-adjacent domain experience; time-series or vehicle data experience is a plus.
Familiarity with MLOps concepts such as CI/CD for ML, model registries, monitoring, and retraining.
Front-end/full-stack delivery experience with dashboards, internal tools, or product UIs.
Track record as a player-coach, leading a team while continuing to contribute code.
AWS experience is preferred.

Responsibilities

Lead and manage an AI/ML engineering team, providing day-to-day direction, coaching, performance management, and career development.
Contribute to hiring by interviewing candidates, raising the engineering craft bar, and helping grow the team.
Develop engineers toward full-stack AI/ML capability across data, modeling, services, and application layers through code review and pairing.
Personally own and ship complex production solutions end-to-end, including data pipelines, model and inference services, agentic/LLM components, APIs, and applications or dashboards.
Spend a substantial share of time building and reviewing code.
Design and implement ML models using structured, time-series, and unstructured data; own validation, evaluation, and error analysis for team deliverables.
Own the team's delivery of production AI/ML solutions and their production health end-to-end.
Set and enforce engineering standards for architecture, code review, testing, and CI/CD.
Work closely with the MLOps team, build to its readiness standards and release gates, and route production-driven code changes back through the team.
Partner with program and product-management teams on scope, priorities, and delivery commitments, communicating progress with evidence.
Coordinate with functional leads and globally dispersed project teams and build products with global platforms and standards.