Lead Palantir Forward Deployed Engineer - GPS
PowerToFly, Florida, NY, United States
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 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 (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 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)
The Team
Deloitte's Government & Public Services (GPS) practice, including our AI & Data offering, provides a full spectrum of solutions for designing, developing, and operating cutting‑edge data and AI platforms, products, insights, and services. The team serves federal, state, & local government clients as well as public higher education institutions, bringing fresh perspectives to help clients anticipate disruption and fulfill their mission promise.
Qualifications
Required:
Bachelor's degree (or equivalent) in Computer Science, Data Science, or Engineering.
10+ 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 Foundry, AIP, or 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.
Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future.
Ability to obtain and maintain a U.S. government security clearance.
Preferred:
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.
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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 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 (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 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)
The Team
Deloitte's Government & Public Services (GPS) practice, including our AI & Data offering, provides a full spectrum of solutions for designing, developing, and operating cutting‑edge data and AI platforms, products, insights, and services. The team serves federal, state, & local government clients as well as public higher education institutions, bringing fresh perspectives to help clients anticipate disruption and fulfill their mission promise.
Qualifications
Required:
Bachelor's degree (or equivalent) in Computer Science, Data Science, or Engineering.
10+ 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 Foundry, AIP, or 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.
Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future.
Ability to obtain and maintain a U.S. government security clearance.
Preferred:
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.
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