Lead Forward Deployed Engineer, Palantir

About the role

Forward Deployed Engineers (FDE) help clients turn AI ambition into enterprise-scale impact, pairing leading class engineering with pod-based delivery and vertical expertise. This role operates at the intersection of product, engineering, problem-solving, and client impact, at the forefront of AI transformations.

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. 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, 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 product, data, and platform leaders
• Lead executive-level discovery, define success metrics (quality, latency, cost, adoption, risk) and a phased plan from prototype to production and scaling
• 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 engagements, ensuring reliable architecture and consistent client experience
• Mentor and develop junior FDEs

GenAI Solution Development

• Architect and oversee delivery of LLM-enabled applications including copilots, agentic workflows, assistants, and knowledge search experiences using one or more enterprise AI platforms
• 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 to production-quality code
• Guide architecture of data pipelines powering GenAI use cases
• Enforce strong data management, testing, CI/CD, logging, versioning, and documentation practices
• Deep familiarity with cloud environments (AWS, Azure, and/or Google Cloud)

Required Qualifications

• Bachelor's degree (or equivalent) in Computer Science, Data Science or Engineering
• 7+ years of experience in software engineering, data engineering, data science, or analytics engineering
• 1+ years of hands-on experience building and deploying GenAI/LLM-powered solutions in client or production environments
• 1+ years of experience with Palantir including hands-on experience with one of the following key platforms: Foundry, AIP, Maven
• 1+ years of experience leading project workstreams/engagements and translating business problems into AI solutions
• 1+ years of experience building reliable, maintainable, and well-documented code
• Ability to travel 50%, on average, based on the work you do and the clients and industries/sectors you serve
• Limited immigration sponsorship may be available

Preferred Qualifications

• Experience with cloud environments (AWS, Azure, and/or Google Cloud) and common platform services (storage, compute, IAM, networking)
• Demonstrated ability to work directly alongside client technical teams and program stakeholders in fast-paced, ambiguous delivery environments
• Data engineering experience with Spark, Airflow/dbt, streaming, data modeling or ML/data science background feature engineering, experimentation or model evaluation
• Experience with MLOps/LLMOps practices: evaluation frameworks, model monitoring, and prompt management
• Experience integrating LLM solutions with enterprise systems via APIs, microservices, or event-driven architectures
• Experience operating within hybrid onshore/offshore teams
• Familiarity with security, privacy, and compliance considerations

Pay

A reasonable estimate of the current range is $189,200 to $372,900. You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.