AI Product Leader

The AI Coding Product Leader defines the vision, strategy, and roadmap for coding agent capabilities across the Stellantis software engineering ecosystem. This role oversees platforms and agentic development capabilities designed to improve developer productivity, strengthen software quality, and increase overall engineering effectiveness. Role Accountabilities Strategy and roadmap Define and communicate a clear vision, strategy, and multi-year roadmap for coding agent capabilities aligned with Stellantis software engineering, AI, and digital transformation priorities. Set platform direction and prioritize investments based on business value, developer productivity, software quality, scalability, risk, and enterprise needs. Platform delivery and adoption Lead the evaluation, rollout, and continuous improvement of solutions such as GitHub Copilot, Claude Code, Codex, OpenCode, and related agentic coding capabilities. Translate strategic priorities into clear epics, target outcomes, adoption plans, and delivery roadmaps for internal teams and external partners. Establish the operating model for coding agents, including onboarding, enablement, usage policies, support, and collaboration across engineering communities. Team and partner leadership Build, grow, and organize the team and ecosystem required to support the coding agent strategy, including internal capabilities, ways of working, and regional coordination. Coordinate external suppliers and strategic partners to ensure delivery quality, cost control, alignment with Stellantis standards, and execution against roadmap commitments. Performance and governance Define and track KPIs such as adoption, active usage, productivity uplift, cycle-time reduction, code quality improvement, and measurable engineering and business impact. Work closely with engineering leaders, enterprise architecture, cybersecurity, legal, procurement, finance, and operations teams to ensure consistent execution and governance. Ensure coding agent capabilities are deployed responsibly, with guardrails for security, compliance, privacy, intellectual property protection, and enterprise risk management. Profile Leadership and delivery Strong experience in platform leadership, software engineering, developer tooling, or engineering transformation. Proven ability to lead cross-functional teams and drive adoption in a complex global organization. Technical and functional understanding Solid understanding of coding agents, AI-assisted software engineering, developer workflows, DevOps practices, and enterprise delivery models. Stakeholder and governance skills Strong stakeholder management, communication, execution, and governance capabilities. Success Measures Strategic alignment A clear and credible coding agent strategy and roadmap are established and supported by key Stellantis stakeholders. Adoption and scale Coding agent capabilities are adopted at scale across software engineering teams and regions. The team and operating model are structured to sustain the strategy and execution model over time. Execution quality Suppliers and partners deliver effectively against objectives, with strong alignment to quality, cost, timelines, and enterprise standards. Value and trust The value delivered by coding agents is visible through well-defined KPIs, regular tracking, and demonstrated engineering and business outcomes. Coding agent capabilities are trusted, secure, and aligned with enterprise governance requirements. Core Qualifications Bachelor’s or Master’s degree in Computer Science, Software Engineering, AI, Data Science, or related field. Minimum 8 years of experience in software engineering, platform engineering, DevOps, AI/ML, or developer tooling. Minimum 3 years of experience with AI-assisted development, coding agents, LLM-based systems, or developer productivity platforms. Proven experience leading technical teams, platforms, or AI transformation initiatives. Experience working in complex global organizations with multiple engineering teams, regions, and stakeholders. Technical Skills Strong understanding of large language models, generative AI, coding agents, and AI-assisted software engineering. Hands‑on or functional experience with tools such as GitHub Copilot, Claude Code, Codex, OpenCode, or similar. Good understanding of SDLC, DevOps practices, CI/CD, testing, code quality, and modern software architectures. Experience evaluating, deploying, or managing AI coding tools and agentic development capabilities. Familiarity with prompt engineering, agent workflows, orchestration frameworks, RAG, embeddings, and AI‑enabled development environments. Ability to assess AI coding solutions based on productivity, quality, scalability, security, integration, cost, and enterprise readiness. Leadership & Strategy Define and execute the vision, strategy, and roadmap for coding agent capabilities across the software engineering ecosystem. Set platform direction and prioritize investments based on business value, developer productivity, software quality, scalability, cost, and risk. Lead the evaluation, rollout, adoption, and continuous improvement of AI coding solutions. Translate strategic priorities into clear epics, outcomes, adoption plans, and delivery roadmaps. Build and structure the team, operating model, and partner ecosystem required to scale coding agent capabilities. Drive adoption across engineering communities through onboarding, enablement, communication, and change management. AI & Automation Expertise Deep understanding of AI‑assisted coding workflows, coding agent design patterns, prompt engineering, and code generation optimization. Experience implementing AI‑driven productivity improvements across development, testing, documentation, code review, and automation workflows. Ability to define validation practices for AI‑generated code, including testing, quality gates, security checks, and human review. Understanding of how coding agents integrate with enterprise platforms such as GitHub, CI/CD, artifact repositories, security tools, and developer portals. Business & Collaboration Skills Strong ability to translate business and engineering problems into AI‑enabled software development solutions. Experience building ROI‑driven business cases for AI adoption, including productivity uplift, cycle‑time reduction, quality improvement, and cost optimization. Ability to partner with engineering, product, cybersecurity, legal, procurement, finance, operations, and enterprise architecture teams. Excellent communication skills for both technical and non‑technical audiences, including executive stakeholders. Experience coordinating external suppliers and strategic partners to ensure delivery quality, cost control, and alignment with enterprise standards. Governance & Risk Management Ability to balance innovation with risk, governance, scalability, cybersecurity, compliance, and enterprise constraints. Understanding of responsible AI, data privacy, intellectual property protection, and security considerations related to AI‑generated code. Experience implementing guardrails for AI‑assisted development, including usage policies, security controls, auditability, and compliance requirements. Ability to define governance models for coding agents, including onboarding, support, approved tools, monitoring, and escalation processes. Preferred / Nice‑to‑Have Experience with enterprise‑scale AI transformation initiatives. Background in DevOps, platform engineering, cloud engineering, MLOps, or developer experience. Knowledge of vector databases, RAG, embeddings, agent frameworks, orchestration frameworks, and AI gateway patterns. Experience leading an AI Center of Excellence, developer productivity team, platform team, or engineering transformation initiative. Experience with GitHub Enterprise, CI/CD platforms, artifact management, testing automation, software quality platforms, or internal developer platforms. #J-18808-Ljbffr