Lead the architecture and development of AI platforms focused on internal process excellence
Partner with the Automotive Principal Architect to define architecture, tools, and processes for AI/ML adoption globally
Manage a team of five AI practitioners and mentor global teams implementing AI solutions
Design and implement advanced AI solutions requiring cross–value-stream orchestration
Lead development of end-to-end data platforms for agentic AI training, evaluation, and reinforcement
Build scalable, real-time pipelines to ingest, transform, and store multimodal data (text, logs, user interactions, actions, environments)
Design dynamic memory and knowledge systems (vector stores, graph DBs, memory stores)
Collaborate with researchers to integrate live feedback, human-in-the-loop data, and auto-labeling systems
Own data governance, quality, and security policies for self-improving AI agents, aligning with global standards
Build observability tools for agent behavior and decision tracking (lineage, reproducibility, versioning)
Mentor junior engineers and cultivate a high-performance engineering culture
Stay ahead of AI infrastructure trends and incorporate best practices into roadmaps
Act as subject matter expert for AI engineering internally and externally
Manage budgets for AI tools and measure value generated vs. costs
Drive ecosystem partnerships, vendor management, and executive-level influence to advance adoption
Success measures: division-wide AI adoption; delivery of scalable, enterprise-grade platforms; number of production AI agents driving measurable efficiency and effectiveness gains
Requirements
Graduate degree in Computer Science, Engineering, or related field (PhD a plus)