Lead Forward Deployed Engineer, Palantir

Deloitte•Jersey City, NJ

155d•$189,200 - $372,900•Hybrid

About The Position

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. 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.

Requirements

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

Nice To Haves

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

Responsibilities

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.
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
Architect and oversee 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.
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)