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About This Role
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.
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.
Basic 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 Qualifications
- 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.
Our Benefits - Designed with You in Mind
Comprehensive Health & Well-being Coverage From your very first day, you'll have access to medical, dental, vision, and prescription drug coverage - ensuring you and your family stay healthy and protected.
Generous Paid Time Off We believe in work-life balance. That's why we offer: 17+ paid holidays, including shut-down from December 24th through New Years Day every year. Vacation, float & wellbeing days, sick time and fully paid parental leave when your family needs you most.
Competitive Retirement Savings Plans We help you plan for the future with:
- An employer match on contributions to your 401k, Roth, and Catch-Up plans
- An employer contribution, even if you don't contribute
Income Protection & Insurance Options Benefit from included and optional disability, life, and other insurance programs - because your peace of mind matters.
Company Vehicle Lease Program Eligible employees and their immediate families can enjoy company vehicle lease options with included insurance, maintenance, and unlimited mileage. Plus, take advantage of exclusive discounts on Stellantis products.
Family Building Benefit We proudly support all paths to parenthood- including fertility and infertility treatments, adoption services, and gestational surrogacy.
Support for Your Growth and Giving Back We believe in investing in your future and your passions:
- Tuition reimbursement
- Student loan refinancing programs
- 18 paid volunteer hours each year to make a difference in your community
And so much more! When you join us, you're not just building a career - you're joining a company that supports you, inside and outside of work.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Stellantis, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Stellantis AI Hiring
Stellantis has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Auburn Hills, MI, US.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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