AI Strategy, Senior Technical Program Manager, Hardware Strategy & Operations

Key job responsibilities

• Set the AI Transformation Strategy — Define and own the multi-year AI roadmap for Hardware Engineering, aligning org-wide investments with business priorities and influencing executive decisions on capability bets, build-vs-buy, and platform direction.
• Partner on Architecture of Agentic Systems for Engineering Workflows — Lead the design and deployment of AI agents, multi-agent orchestration patterns, and RAG-based systems that automate and augment hardware design, validation, supply chain, and program management workflows.
• Drive Org-Wide Adoption & Change — Own the adoption strategy for AI capabilities across thousands of engineers; build the coalitions, incentives, and operating mechanisms that move AI from pilot to default practice.
• Govern a Strategic Portfolio — Establish the governance, prioritization, and resource-allocation frameworks for the full AI portfolio; chair cross-functional steering reviews and make trade-off calls that span multiple VPs.
• Partner Deeply on Technical Direction — Work shoulder-to-shoulder with SDEs and platform teams to shape model selection, evaluation frameworks, MLOps practices, and orchestration architecture; provide credible technical pushback and direction.
• Influence Without Authority at the Highest Levels — Communicate strategy, risks, and outcomes through executive narratives, OP1/OP2 documents, and quarterly business reviews; influence VP-level stakeholders and represent the org externally where appropriate.
• Build Repeatable AI Operating Models — Codify successful patterns into reusable playbooks, reference architectures, and adoption frameworks that scale beyond your immediate org.
• Define Outcome Metrics That Matter — Establish the success criteria, north-star metrics, and ROI frameworks for AI investment; tie program outcomes directly to engineering velocity, product quality, and unit economics.

Basic Qualifications

• 7+ years of working directly with engineering teams experience
• 5+ years of technical product or program management experience
• 3+ years of technical program management working directly with software engineering teams experience
• Experience managing programs across cross functional teams, building processes and coordinating release schedules
• Bachelor's degree or equivalent in a related technical field
• Demonstrated experience establishing AI systems, AI agents, or agent orchestration frameworks for process automation, decision support, or engineering workflow improvement
• Proven track record owning programs end-to-end with full autonomy — from ambiguous problem framing through funding, staffing, execution, and measured business impact
• Hands-on familiarity with the modern AI/ML stack: LLMs, RAG architectures, vector databases, agent frameworks (e.g., LangChain, LangGraph, AutoGen, Bedrock Agents), evaluation harnesses, and MLOps tooling
• Deep fluency with software tools, data systems, and analytical platforms (e.g., SQL, Python, Tableau/QuickSight, JIRA, Confluence)
• The ability to interrogate technical implementations directly
• Experience influencing and aligning senior leaders (Director, VP, SVP) across multiple organizations on technical strategy and investment
• Exceptional written communication: experience authoring executive narratives, strategy documents, and operating-plan submissions for senior leadership

Preferred Qualifications

• Master's degree in computer science, engineering, analytics, mathematics, statistics, IT or equivalent, or Bachelor's degree in Electrical or Mechanical Engineering
• MBA, PgMP, PMP, or equivalent senior program management certification, or equivalent demonstrated leadership
• Experience influencing and aligning senior leaders (Director, VP, SVP) across multiple organizations on technical strategy and investment
• Exceptional written communication: experience authoring executive narratives, strategy documents, and operating-plan submissions for senior leadership
• Proven track record owning programs end-to-end with full autonomy — from ambiguous problem framing through funding, staffing, execution, and measured business impact
• Hands-on familiarity with the modern AI/ML stack