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
• 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
• Career development and mentoring 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
• Set direction for prompt engineering, tool-use patterns, and human-in-the-loop controls
• Guide architecture of data pipelines powering GenAI use cases
• Enforce strong data management, testing, CI/CD, logging, versioning, and documentation practices

Client Engagement

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

Cross-Functional Pod Leadership & Program Governance

• Coordinate multi-pod or multi-workstream engagements, ensuring reliable architecture and consistent client experience

Engineering & Data Foundations

• Review and contribute to production-quality code
• Deep familiarity with cloud environments (AWS, Azure, and/or Google Cloud)

The team

AI & Engineering leverages cutting-edge engineering capabilities to build, deploy, and operate integrated/verticalized sector solutions in software, data, AI, network, and hybrid cloud infrastructure. These solutions are powered by engineering for business advantage, transforming mission-critical operations. We enable clients to stay ahead with the latest advancements by transforming engineering teams and modernizing technology & data platforms. Our delivery models are tailored to meet each client's unique requirements.

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

Wage Range

The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The 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. A reasonable estimate of the current range is $189,200 to $372,900.

Benefits

We are committed to providing a diverse and inclusive workplace where everyone is treated with dignity and respect. We are proud to be an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability status, protected veteran status, or any other characteristic protected by law. We also provide reasonable accommodations for qualified individuals with disabilities and individuals who are pregnant, have parents with newly born or adopted children, or who have recently experienced a miscarriage.