About the Role:Our organization is scaling from individual AI initiatives to a durable, enterprise-grade delivery organization. We're looking for someone to lead the multidisciplinary technical team that turns AI use cases into shipped production capabilities - AI Solutions Architects, Data Engineers, Data Scientists, Agentic/Prompt Engineers, and Visualization & Full-Stack Developers. You'll build and run this department: hiring and developing the team, setting engineering standards, and making sure the handoffs between these disciplines are fast and clean rather than a source of friction. You'll work in close partnership with AI Product Management, which owns the use case roadmap and business requirements your team builds against.
This is a player-coach leadership role, not a purely administrative one. You'll set technical and delivery standards, remove roadblocks, and be the person accountable when the portfolio does - or doesn't - ship on time and at quality.
What You'll Do:- Lead and grow a multidisciplinary technical team spanning Solutions Architects, Data Engineers, Data Scientists, Prompt/Agentic Engineers, and Visualization & Full-Stack Developers
- Own end-to-end delivery accountability for the AI use case portfolio: on-time, on-quality delivery from intake through production adoption
- Define how the disciplines on your team work together - clear ownership boundaries, handoff points, and working agreements between roles that could otherwise overlap (e.g., architecture vs. engineering, data science vs. engineering)
- Set and enforce engineering and delivery standards: code quality, model validation rigor, architecture review, documentation, and production readiness criteria
- Own hiring, onboarding, performance management, and career development for the department; build the org structure as headcount scales
- Partner closely with the AI Product Management organization, translating their roadmap and business requirements into a resourced, sequenced technical delivery plan
- Manage capacity planning and resource allocation across concurrent use cases, balancing quick wins against larger strategic builds
- Ensure every use case moves through the department's governance process (risk tiering, intake, architecture review) efficiently, without becoming a bottleneck to delivery
- Own the technology and tooling strategy for the department, including the role of Microsoft Copilot / Copilot Cowork, Snowflake, and other core platforms in the team's delivery approach
- Report delivery performance, capacity, and portfolio health to enterprise leadership; own escalations when priorities conflict or delivery is at risk
- Represent the engineering organization in cross-functional planning with legal, security, IT, and business unit stakeholders
Basic Qualifications:- Bachelor's degree in Computer Science, Engineering, or related field
- Minimum 8 years in engineering or technical leadership roles, with 3+ years directly managing engineers, data scientists, or similar technical disciplines
- Demonstrated experience leading multidisciplinary technical teams (data engineering, data science, and/or software engineering) through the full build-to-production lifecycle
- Strong technical fluency across the AI/ML stack - enough to evaluate architecture decisions, review technical tradeoffs, and earn credibility with senior engineers, even if you're not hands-on daily
- Track record of building or significantly scaling a technical team or function, including hiring and org design
- Experience running delivery in a large, matrixed enterprise, balancing speed with governance, security, and risk requirements
- Strong stakeholder management skills - comfortable translating between technical teams and business/executive audiences in both directions
- Sound judgment on prioritization and resourcing trade-offs under real capacity constraints
Preferred Qualifications:- 10+ years in engineering or technical leadership roles, with 5+ years directly managing engineers, data scientists, or similar technical disciplines
- Advanced degree in Computer Science, Engineering, or a related field
- Prior automotive OEM or manufacturing enterprise experience
- Experience standing up an AI or data function from early stage to scaled department
- Direct experience with Microsoft Copilot / Copilot Cowork and Snowflake in an enterprise delivery context
- Experience operating within a formal AI governance framework (risk tiering, model review, responsible AI standards)
