Technical Lead, AI Engineering (Sales, Service & Supply Chain)

About Lucid
At Lucid, we are creating exceptional mobility experiences through innovation to drive the world forward. Built on Lucid’s proprietary technology and software-defined vehicle architecture, our award-winning vehicles bring our “Compromise Nothing™” approach to the global automotive market. That means refusing to choose between performance and sustainability, design and engineering, ambition and integrity. In Lucid Air and Lucid Gravity, we have designed and built vehicles that have redefined their segments, combining exceptional range, performance, design, and expansive space in a single experience.
We achieve this through deep vertical integration, with design, engineering, and production happening in-house across our global offices and manufacturing facilities. Our teams come from industries around the world, united by a shared commitment to excellence. By refusing to settle, you can help redefine what’s possible and shape the future of mobility.
About the role
Lucid is putting an AI layer over the systems that run our commercial and industrial operations, how we sell vehicles, how we service them, and how we move parts through a global supply chain. This role sets the technical direction for that work and is accountable for what reaches production.
You will sit close to the business. Sales, Service and Supply Chain leaders will bring you problems, and you will decide what is worth building, design it, build the hard parts yourself, and lead a distributed engineering team to deliver the rest. This is a hands-on lead role, architecture and code, not a management track.
What you'll do
Own the architecture for LLM-based and agentic systems across Sales, Service and Supply Chain: assistants over enterprise knowledge, agents that act against internal APIs, document and data extraction pipelines, and the platform pieces they share.
Deliver those systems into the channels our customers and employees already use: web and in-app chat, email and SMS. Own the conversational contract across them, including threading, context carry-over, handoff to a human, and tone appropriate to the channel.
Turn business problems into engineering plans. Work directly with stakeholders to define the outcome, then sequence the work so value lands early and often.
Build the parts that make AI safe to run in the enterprise: input and output guardrails, identity-aware access to tools and data, prompt governance, and clear trust boundaries between internal and external-facing surfaces.
Own the tool layer, including MCP servers and internal tool interfaces, so agents reach business systems through contracts that are versioned, permissioned and testable.
Define how quality is measured, including evaluation datasets, regression suites, tracing and production telemetry, and make it the default gate for prompt and model changes.
Own the unit economics. Set targets for cost and latency per conversation and per resolution, and hold them through model routing, context trimming, caching, batching and right-sized retrieval.
Integrate with systems of record: ERP, supplier portals, service and CRM platforms, logistics and planning systems. The interesting problems here are data, integration and co