AI Solutions Engineering - North America

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.

• →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

• 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

• 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)