Lead Palantir Forward Deployed Engineer - GPS

About the role

The Deloitte AI & Engineering team transforms technology platforms and drives innovation for clients. You will work with talented professionals to develop and deploy GenAI solutions into production.

Responsibilities

• Serve as the senior practitioner-leader embedded directly with strategic clients, leading forward-deployed engineering pods.
• Set technical direction, remove delivery blockers, and stay hands-on in designing, reviewing, and debugging systems.
• Translate engineering trade-offs into clear decisions for client leaders.
• Represent Deloitte's FDE capability in client pursuits, executive briefings, and platform partner engagements.
• Lead FDE pods of 2-5 engineers, owning execution, resource management, escalations, and overall delivery health.
• Coordinate multi-pod or multi-workstream engagements, ensuring reliable architecture and a 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.
• Define evaluation frameworks covering quality, hallucination risk, safety, latency, cost, and governance.

Requirements

• Bachelor's degree (or equivalent) in Computer Science, Data Science, or Engineering.
• Active US government security clearance.
• 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, 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% based on the work you do and the clients and industries/sectors you serve.

Qualifications

• Experience with cloud environments (AWS, Azure, and/or Google Cloud).
• 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.