Lead, AI Product Engineer, Facilities Technology

Rivian•Riverdale, GA

4h•Hybrid

About The Position

Rivian is on a mission to keep the world adventurous forever. This goes for the emissions-free Electric Adventure Vehicles we build, and the curious, courageous souls we seek to attract. As a company, we constantly challenge what’s possible, never simply accepting what has always been done. We reframe old problems, seek new solutions and operate comfortably in areas that are unknown. Our backgrounds are diverse, but our team shares a love of the outdoors and a desire to protect it for future generations. We're seeking a Lead, AI Engineer to independently design, build, and deploy AI-powered products and workflows that deliver real operational savings and improvements across Rivian's Facilities organization. This is a new kind of role — part product owner, part developer, part designer — built for the era of LLM-augmented work. You won't wait for engineering bandwidth. You'll use AI-native tools like Cursor, Gemini, Claude, and Glean to independently ship working solutions, closing the gap between an organizational problem and a working product. You own your solutions end-to-end: from identifying and scoping a costly manual process, to building the automation that replaces it, to proving the benefits after deployment.

Requirements

7+ years in a technical role — software development, product ownership, technical program management, or similar
Bachelor's degree in Computer Science, Software Engineering, Information Systems, or a related technical field; OR equivalent practical, hands-on experience in lieu of a degree
Demonstrated experience building enterprise apps with AI-assisted coding tools (Cursor, GitHub Copilot, Claude Code, OpenAI Coxed, and equivalent)
Working knowledge of prompt and skill engineering, AI agent design and orchestration, and LLM application development
Ability to connect systems via APIs and configure workflow automations end-to-end
Strong UI/UX instincts — can produce functional, clean interfaces without a design team
Excellent communication skills; equally comfortable in a whiteboard session with leadership or a working session with ops teams
Self-directed; thrives in ambiguous environments and can quantify and communicate the business impact of technical work in terms of cost savings, time reduction, and operational efficiency

Nice To Haves

Familiarity with large-scale construction, real estate, or capital programs
Experience with enterprise AI tools like Glean or similar knowledge management platforms
Exposure to MCP (Model Context Protocol) frameworks and multi-agent architectures
Prior experience shipping internal tools in a non-engineering business unit
Track record of upskilling peers or running internal training on new technologies

Responsibilities

Design, build, and deploy internal applications, agents, and multi-step automations using LLM-assisted development tools (Cursor, Gemini, Claude, Glean, etc.) targeting the highest-cost manual processes across the Facilities org, such as project reporting, cost tracking, change order management, schedule forecasting, document review, cross-functional coordination, and vendor coordination
Connect Facilities platforms (ACC, Procore, Kahua, FOS, Databricks) via APIs and MCP integrations to create seamless, intelligent workflows that unify siloed data and eliminate duplicate work
Stand up production-ready enterprise solutions where speed and simplicity are prioritized over engineering complexity.
Own the Full SDLC by applying traditional product development rigor to AI-generated code. You will manage sprint cycles, define technical requirements in Jira, and oversee the end-to-end lifecycle of the tools you build.
Act as the ultimate gatekeeper for quality. You will conduct rigorous code reviews on both human- and AI-written code, ensuring enterprise-grade security, scalability, and clean UI/UX design.
Translate ambiguous operational problems from the Facilities team into well-structured technical architecture, using AI tools not as a crutch, but as an accelerator for rapid prototyping and deployment.
Quantify the value of every major solution: hours saved, cost avoided, errors eliminated. If you can't measure it, rethink the approach
Coach Facilities team members who are developing their own AI solutions, helping them get over technical and conceptual hurdles
Contribute to informal workshops, demos, and office hours to grow AI fluency across the Facilities organization
Create reusable skills, plugins, playbooks, and how-to guides so that good solutions scale beyond a single use case
Partner with cross-functional Facilities stakeholders such as project managers, construction and design leads, real estate, ops, and finance to identify the operational bottlenecks that create the most risk or opportunity for improvement.
Maintain a deep working knowledge of the Facilities tech stack (Autodesk Construction Cloud, Revit, Kahua, and proprietary system) to build solutions that fit how people actually work
Communicate impact to leadership in business terms — dollars, days, headcount equivalents — not just technical metrics