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

Lucid Motors•Newark, CA

15d•$216,600 - $317,680

About The Position

Lucid is implementing an AI layer across its commercial and industrial operations, encompassing sales, service, and global supply chain logistics. This role is pivotal in setting the technical direction for this initiative and ensuring its successful production deployment. The position requires close collaboration with business leaders, who will present challenges that the Technical Lead will assess for viability, design solutions for, personally develop critical components, and guide a distributed engineering team in completing the implementation. This is a hands-on role focused on architecture and coding, not a management position.

Requirements

10+ years of experience building and operating production software.
3+ years of experience owning the technical direction of a team or a significant platform.
Strong Python skills and solid distributed systems fundamentals.
Comfortable owning services end-to-end, including post-deployment.
Real production experience with LLM applications, including retrieval-augmented generation, agent and tool-calling patterns, structured output, streaming, and prompt design/iteration under load.
Strong hands-on experience with LLM engineering tooling like Langfuse or LangSmith for tracing, prompt versioning, evaluation runs, and production monitoring.
Good working experience with MCP: building and consuming MCP servers, designing tool schemas, and handling authentication and permissions at the tool boundary.
Practical command of LLM cost optimization techniques: model routing and tiering, context and prompt compression, prompt caching, batching, token budgeting, and cost management.
Experience delivering AI over customer and employee communication channels (chat, email, SMS) and their integrations.
Judgment on the LLM landscape (hosted models, managed services, fine-tuning) to make defensible choices on cost, latency, and accuracy.
Strong AWS engineering experience (or equivalent), including serverless and container workloads, event-driven design, IAM, networking basics, and cost awareness at scale.
Experience with the data layer behind AI systems (relational, document, vector) and maintaining retrieval accuracy.
A concrete point of view on evaluating AI systems and the discipline to build necessary infrastructure before scaling features.
Experience working with non-technical stakeholders and translating business intent into system design.

Nice To Haves

Automotive, manufacturing, logistics, or another operations-heavy domain background.
Exposure to SAP, supply chain execution, EDI, or field service and CRM platforms.
Hands-on work with messaging and contact center platforms such as Twilio, Amazon SES or Pinpoint, Salesforce Service Cloud, or similar.
Security-sensitive AI work: prompt injection defense, tenant or supplier data isolation, red-teaming.
Multi-agent orchestration, or experience running an evaluation program at scale.
Experience leading distributed teams across US and India time zones.

Responsibilities

Own the architecture for LLM-based and agentic systems across Sales, Service and Supply Chain, including assistants over enterprise knowledge, agents acting against internal APIs, document and data extraction pipelines, and shared platform components.
Deliver these systems into existing customer and employee channels such as web and in-app chat, email, and SMS, managing the conversational contract across them (threading, context carry-over, human handoff, and channel-appropriate tone).
Translate business problems into engineering plans, working directly with stakeholders to define outcomes and sequence work for early and frequent value delivery.
Build AI safety features for enterprise use, including input/output guardrails, identity-aware access to tools and data, prompt governance, and clear trust boundaries.
Own the tool layer, including MCP servers and internal tool interfaces, ensuring agents interact with business systems through versioned, permissioned, and testable contracts.
Define quality measurement standards, including evaluation datasets, regression suites, tracing, production telemetry, and implement them as gates for prompt and model changes.
Own unit economics by setting and meeting targets for cost and latency per conversation/resolution through model routing, context trimming, caching, batching, and right-sized retrieval.
Integrate with systems of record such as ERP, supplier portals, service and CRM platforms, and logistics and planning systems, focusing on data, integration, and correctness.
Set standards for the wider team, including shared services, reusable patterns, and review expectations, ensuring their effectiveness beyond personal code.
Lead technically across time zones, partnering with the engineering team in India, mentoring engineers, reviewing designs, and serving as a point of contact for complex problems.
Provide honest assessments of AI's applicability, preventing costly detours.
Lead technically across time zones, including close partnership with our engineering team in India. Mentor engineers, review designs, and be the person others bring their hardest problem to.