Lead Forward Deployed Engineer, Palantir

Work you’ll do

• Serve as the senior client-facing presence, building trusted advisor relationships as the senior engineering partner for client product, data, and platform leaders
• 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
• Arcitect 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
• 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)

Client Engagement

• Translate engineering trade-offs into clear decisions for client leaders when needed

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

Engineering & Data Foundations

• 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
• Experience with cloud environments (AWS, Azure, and/or Google Cloud) and common platform services (storage, compute, IAM, networking)
• 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

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

The wage range for this role

Disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case.

Discretionary annual incentive program

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.

Additional resources

• Check out recruiting tips from Deloitte recruiters
• Learn more about what working at Deloitte can mean for you

Inclusive culture

Our inclusive culture empowers our people to be who they are, contribute their unique perspectives, and make a difference individually and collectively. It enables us to leverage different ways of thinking, ideas, and perspectives, and bring more creativity and innovation to help solve our clients' most complex challenges. This makes Deloitte one of the most rewarding places to work.