Lead Palantir Forward Deployed Engineer - GPS

Our Deloitte AI & Engineering team works to transform technology platforms, drive innovation, and help make a significant impact on our clients’ success. You’ll work alongside talented professionals reimagining and reengineering operations and processes that are critical to businesses. Your contributions can help clients improve financial performance, accelerate new digital ventures, and fuel growth through innovation. 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 pr [... source excerpt omitted ...] tional complexity and influence to align executive sponsors, information technology 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 [... 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 contribut