Lead Forward Deployed Engineer, Palantir

About the role

Forward Deployed Engineers (FDE) at Deloitte help clients transform AI ambition into enterprise-scale impact by pairing leading-class engineering with pod-based delivery and vertical expertise.

Responsibilities

• 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.
• Set technical direction, remove delivery blockers, and stay hands-on; designing, reviewing, and debugging systems with the team.
• Translate engineering trade-offs into clear decisions for client leaders when needed.
• Represent Deloitte's FDE capability in client pursuits, executive briefings, and platform partner engagements.
• Lead FDE pods of 2-5 onshore anchored and offshore supported engineers, owning execution, resource management, escalations, and overall delivery health.
• Define end-to-end RAG pipeline design, ensuring production-grade quality and scalability.
• Guide architecture of data pipelines powering GenAI use cases.
• Operate within hybrid onshore/offshore teams.

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 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.

Qualifications

• 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.

Preferred 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.
• Familiarity with security, privacy, and compliance considerations.