AI Coding Product Leader

Stellantis•Auburn Hills, MI

About The Position

The AI Coding Product Leader defines the vision, strategy, and roadmap for coding agent capabilities across the Stellantis software engineering ecosystem. The role oversees platforms and agentic development capabilities intended to improve developer productivity, strengthen software quality, and increase engineering effectiveness. The role leads the evaluation, rollout, adoption, and continuous improvement of coding-agent solutions; sets platform direction and priorities; and translates strategy into epics, outcomes, adoption plans, and delivery roadmaps. It establishes the operating model for coding agents, including onboarding, enablement, usage policies, support, and collaboration across engineering communities. The leader builds and organizes the team and partner ecosystem, coordinates suppliers, and works with engineering, architecture, cybersecurity, legal, procurement, finance, and operations stakeholders. The role defines and tracks performance measures and ensures coding-agent capabilities are deployed responsibly with security, compliance, privacy, intellectual-property, and enterprise-risk safeguards.

Requirements

Bachelor’s or Master’s degree in Computer Science, Software Engineering, AI, Data Science, or a related field.
At least 8 years of experience in software engineering, platform engineering, DevOps, AI/ML, or developer tooling.
At least 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.
Strong understanding of large language models, generative AI, coding agents, and AI-assisted software engineering.
Hands-on or functional experience with GitHub Copilot, Claude Code, Codex, OpenCode, or similar tools.
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 for productivity, quality, scalability, security, integration, cost, and enterprise readiness.
Strong 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.
Understanding of coding-agent integration with enterprise platforms such as GitHub, CI/CD, artifact repositories, security tools, and developer portals.
Strong ability to translate business and engineering problems into AI-enabled software development solutions.
Ability to partner with engineering, product, cybersecurity, legal, procurement, finance, operations, and enterprise architecture teams.
Excellent communication skills with technical and non-technical audiences, including executive stakeholders.
Experience coordinating external suppliers and strategic partners.
Ability to balance innovation with governance, scalability, cybersecurity, compliance, and enterprise constraints.
Understanding of responsible AI, data privacy, intellectual-property protection, and security considerations for AI-generated code.
Strong stakeholder management, communication, execution, and governance capabilities.

Nice To Haves

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.

Responsibilities

Define and communicate a vision, strategy, and multi-year roadmap for coding-agent capabilities aligned with software engineering, AI, and digital transformation priorities.
Set platform direction and prioritize investments based on business value, developer productivity, software quality, scalability, cost, risk, and enterprise needs.
Lead the evaluation, rollout, adoption, and continuous improvement of coding-agent solutions such as GitHub Copilot, Claude Code, Codex, and OpenCode.
Translate strategic priorities into epics, target outcomes, adoption plans, and delivery roadmaps for internal teams and external partners.
Establish the coding-agent operating model, including onboarding, enablement, usage policies, support, and collaboration across engineering communities.
Build and organize the team and ecosystem needed 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 company standards, and execution against roadmap commitments.
Define and track KPIs such as adoption, active usage, productivity uplift, cycle-time reduction, code quality improvement, and engineering and business impact.
Work with engineering leaders, enterprise architecture, cybersecurity, legal, procurement, finance, and operations to support consistent execution and governance.
Ensure responsible deployment of coding-agent capabilities with safeguards for security, compliance, privacy, intellectual-property protection, and enterprise risk.
Drive adoption through onboarding, enablement, communication, and change management.
Define validation practices for AI-generated code, including testing, quality gates, security checks, and human review.
Develop ROI-driven business cases for AI adoption, including productivity, cycle-time, quality, and cost outcomes.
Implement governance models and guardrails for AI-assisted development, including policies, security controls, auditability, monitoring, and escalation processes.