Lead Forward Deployed Engineer, Palantir

At Deloitte, Forward Deployed Engineers (FDE) don’t just build AI solutions, they help clients turn AI ambition into enterprise-scale impact, pairing leading class engineering with pod-based delivery and vertical expertise. If you thrive at the intersection of product, engineering, problem-solving, and client impact, this role puts you at the forefront of AI transformations. Recruiting for this role ends on 10/30/2026. Work you’ll do As a Lead Palantir FDE, you will serve as the senior practitioner-leader embedded directly with our most strategic clients, leading forward-deployed engineering pods that develop and deploy GenAI solutions into production for Deloitte’s most strategic clients. You’ll set technical direction, remove delivery blockers, and stay hands-on; designing, reviewing, and debugging systems with the team. You’ll translate engineering trade-offs into clear decisions for client leaders when needed. Your ability to influence decisions at the C-suite level, while maintaining hands-on technical credibility, is what sets you apart. Pods under your leadership may be deployed onshore with clients or in hybrid onshore/offshore configurations, leveraging Deloitte's global delivery capability to maximize speed and scale. Client Engagement Serve as the senior client-facing presence, building trusted advisor relationships as the senior engineering partner for client p [... source excerpt omitted ...] ng Navigate organizational complexity and influence to align executive sponsors, IT leadership, and business owners around a shared vision Represent Deloitte's FDE capability in client pursuits, executive briefings, and platform partner engagements—contributing to pipeline development and deal shaping. Cross-Functional Pod Leadership & Program Governance Lead FDE pods of 2–5 onshore anchored and offshore supported engineers, owning execution, resource management, escalations and overall delivery health Enforce delivery standards across the pod: sprint cadences, stakeholder communication plans, risk management, and quality gates Coordinate multi-pod or multi-workstream eng [... source excerpt omitted ...] ee delivery of LLM-enabled applications including copilots, agentic workflows, assistants, and knowledge search experiences using one or more enterprise AI platforms (see Platform Requirements below) Set direction for prompt engineering, tool-use patterns, and human-in-the-loop controls Govern end-to-end RAG pipeline design—including ingestion, chunking, embedding, vector retrieval, and hybrid search—ensuring production-grade quality and scalability. Define evaluation frameworks covering quality, hallucination risk, safety, latency, cost, and governance; ensure the pod meets agreed engineering quality bars to these standards. Engineering & Data Foundations Review and contribute