Palantir Forward Deployed Engineer - GPS

About Deloitte AI & Engineering

Work You'll Do

Client Engagement

• Embed with clients to identify needs and translate high-value GenAI use cases into solutions.
• Partner with leaders, product owners, architects, and engineers to align priorities and delivery.
• Lead working sessions to shape solutions and drive client outcomes.
• Prototype and deliver working AI solutions.
• Contribute independently within an FDE pod and mentor 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.
• Balance quality, safety, latency, cost, and model risk in architecture decisions.
• Deliver production-quality code with testing, CI/CD, logging, versioning, and documentation.
• Design extensible functionality and support sprint sizing.
• Create reusable code, prompt libraries, runbooks, and reference implementations.

Required Qualifications

• Bachelor's degree or equivalent in computer science, data science, engineering, or a related field.
• At least 4 years of experience in software engineering, data engineering, data science, or analytics engineering.
• At least 1 year of hands-on experience building and deploying GenAI or LLM-powered solutions.
• At least 1 year of hands-on Palantir experience with Foundry, AIP, or Maven.
• At least 1 year of leading project workstreams or engagements and translating business problems into AI solutions.
• At least 1 year of building reliable, maintainable, and well-documented code.
• Ability to travel 50% on average.
• US work authorization without employer sponsorship.
• Ability to obtain and maintain a US government security clearance.

Preferred Qualifications

• Experience with AWS, Azure, Google Cloud, and common storage, compute, IAM, and networking services.
• Experience with Spark, Airflow or dbt, streaming, data modeling, feature engineering, experimentation, or model evaluation.
• Experience with MLOps or LLMOps, including evaluation frameworks, model monitoring, and prompt management.
• Experience integrating LLM solutions through APIs, microservices, or event-driven architectures.
• Experience working in hybrid onshore/offshore teams.
• Familiarity with security, privacy, and compliance considerations.

Compensation

• The disclosed compensation range estimate is $134,500 to $265,100.
• Compensation decisions consider skills, experience, training, licensure, certifications, geography, and organizational needs.
• The role may include eligibility for a discretionary annual incentive program.