AI Leader - Global Industrial

Location: Birmingham, AL, USA

About the role

Motion is building an enterprise AI operating model that combines engineering discipline, responsible governance, and broad workforce enablement. The AI Leader will lead this group, responsible for enterprise AI strategy, platform direction, standards, evaluation, and business enablement.

They will drive how AI scales across Motion, balancing off-the-shelf capabilities with custom AI where it creates differentiated value, while putting expertise directly into the business to accelerate adoption.

This is an enterprise leadership role grounded in strong technical credibility. The AI Leader must have experience building and shipping production AI, while leading through strategy, technical direction, talent, and influence. The mandate is simple: turn AI into a scalable enterprise capability that delivers measurable business value.

You must be eligible to work in the US without Visa Sponsorship

What you will own

• AI platform strategy and the enterprise agent platform standard: patterns, reusable components, promotion criteria from prototype to production.
• The model and vendor catalog: which models are approved for which data classes, with a quality and cost gate from your evaluation engineer and a security gate from the CISO organization, feeding one governance intake.
• The policy layer of the AI gateway (Infrastructure operates it; you are accountable for what runs through it).
• The evaluation discipline: automated accuracy, regression, safety, cost, and latency gates before any agent reaches production.
• Outward enablement: forward-deployed engineers advising the business, the citizen-maker program on Copilot Studio with governance zones and fusion teams, and the AI Business Partner network in every major function.
• A first-line seat on the AI Governance Council
• The departmental budget, hiring, and talent bar, including holding the player-coach standard for every engineering hire.
• Identification and prioritization of high-impact AI opportunities across the business: automation, operational efficiency, customer experience, and revenue growth, each with a measurable value case.
• Board-level AI material in support of the CIO: adoption, value, risk posture, roadmap, and competitive positioning.

Experience

Platform engineering: 10+ years. Distributed systems, integration, and the operational discipline that keeps things running. At least one tour owning a platform other teams built on, with a backlog, service levels, and consumers who had alternatives.

Leading engineering teams: 10+ years, including owning a departmental budget and hiring against a bar you set.

Machine learning: 5+ years. Models in production that a business process depended on — forecasting, optimization, ranking, propensity. You know how they degrade, how to detect it, and what it costs when nobody notices for two quarters.

Generative AI: 2+ years. Production GenAI is roughly four years old, so we're measuring shipped systems, not tenure. Show us the LLM, retrieval, or agentic systems you put in front of real users, the evaluation approach behind them, and what broke.

What you must bring

Deep technical fluency with a demonstrated history of building and shipping production AI systems: you have owned models, pipelines, or platforms that real users depended on, can challenge architecture, evaluate technical tradeoffs, and establish engineering standards, and can prototype when valuable. Your technical judgment is current enough to personally evaluate agent architectures and model outputs, not just review summaries of them, and you set technical direction and success metrics for teams doing the same.

Strong working understanding of LLMs, retrieval, and agentic systems, including how to evaluate them for accuracy, cost, latency, and safety.

Product judgment: you can run a platform as a product, with a backlog, service levels, and a promotion pipeline, and you know why 'pilot purgatory' happens and how to prevent it.

Governance fluency: working knowledge of standards for responsible AI, model governance, and explainability, and a 'just enough' governance instinct that balances innovation velocity with risk, satisfying auditors without strangling makers.

Executive presence: you build consensus across functions with competing priorities and different risk appetites, navigate organizational dynamics with high integrity, and can carry a board-level narrative and a whiteboard architecture session in the same afternoon.

Trusted-advisor credibility: experience consulting directly with senior business leaders, translating business problems into technical solutions and helping executives understand what is genuinely possible.

Value discipline: you build cases that quantify AI value in terms leadership recognizes, manage budget and resource allocation across a portfolio, and measure adoption through usage analytics and user feedback, closing the gap between what we think is working and what we know is working.

Vendor strategy: clear-eyed build-versus-buy judgment across the Microsoft ecosystem and model providers.

Enterprise change instincts: adoption is won in branches and back offices, not in demos; you thrive in ambiguity, bring structure to evolving programs, and mentor engineers while establishing patterns for reliable, governed AI systems.

Depth across both predictive and generative AI: you have shipped supervised or forecasting models into production where a business process depended on the output, and you understand why those models degrade, how to detect it, and what it costs when nobody notices for two quarters.

Judgment about when not to use an LLM: you can tell when gradient boosting, a constrained optimizer, or a well-tuned heuristic beats an agent on accuracy, cost, and explainability, and you'll say so in a room that wants the agent.

Nice to have

Distribution, industrial, or B2B domain experience; branch or field operations exposure.

Azure OpenAI, Copilot Studio, and Power Platform governance at enterprise scale.

Experience in a public-company or IPO-preparation environment, including audit and disclosure interaction.

Experience standing up a center of excellence, platform team, or enablement function from scratch.

Experience designing or sponsoring AI training and enablement programs for corporate functions such as sales, supply chain, finance, and HR.

Familiarity with continuous improvement or Lean language and methodology.

Forecasting, inventory optimization, or price/margin science in a multi-SKU, multi-location environment.

Recommendation, search relevance, or product-matching systems at catalog scale.

Databricks, Snowflake, or comparable ML platform experience alongside the Microsoft stack.

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GPC conducts its business without regard to sex, race, creed, color, religion, marital status, national origin, citizenship status, age, pregnancy, sexual orientation, gender identity or expression, genetic information, disability, military status, status as a veteran, or any other protected characteristic. GPC's policy is to recruit, hire, train, promote, assign, transfer and terminate employees based on their own ability, achievement, experience and conduct and other legitimate business reasons.

Additional Locations: Atlanta, GA, USA