Forward Deployed Engineer- Palantir

About the role

Deloitte seeks Forward Deployed Engineers (FDE) who can help clients transform AI ambitions into impactful enterprise solutions. This role combines product, engineering, problem-solving, and client impact.

Responsibilities

• Embed with clients to understand business needs and develop high-value GenAI use cases.
• Partner with leaders, product owners, architects, and engineers to align priorities and deliver solutions.
• Lead working sessions to shape solutions and drive client outcomes.
• Prototype and deliver working AI solutions using industry expertise and emerging capabilities.
• Contribute independently within an FDE pod while mentoring newer team members.

Solution Engineering

• Build AI-enabled solutions, agentic platforms, and workflows across enterprise AI platforms.
• Develop scalable AI engineering patterns, tool-use approaches, and human-in-the-loop controls.
• Deliver production-quality code using strong practices in testing, CI/CD, logging, versioning, and documentation.
• Design extensible functionality, support sprint sizing, and align solutions with senior team members.
• Contribute reusable assets including code, prompt libraries, runbooks, and reference implementations.

Requirements

• Bachelor's degree (or equivalent) in Computer Science, Data Science, or Engineering.
• 3+ 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/products; 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.

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