Senior Forward Deployed Engineer, Palantir
Posted 3 days ago
The role in short
Embed with clients to prototype and deliver production-grade GenAI solutions and agentic platforms using Palantir technologies.
At Deloitte, Forward Deployed Engineers (FDE) don't just build AI solutions, they help clients turn AI ambition into enterprise-scale impact, pairing leading class engineering with pod-based delivery and vertical expertise. If you thrive at the intersection of product, engineering, problem-solving, and client impact, this role puts you at the forefront of AI transformations.
Recruiting for this role ends on 10/30/2026.
Work you'll do
As a Senior Palantir FDE, you will work side by side with senior functional and technical client team members to rapidly prototype and deliver high-impact GenAI-enabled solutions. This requires a highly motivated practitioner who moves with speed and precision, building working software, engaging confidently with senior stakeholders and engineers to bring measurable business impact from day one. Additional responsibilities include:
Client Engagement
- Embed with clients to identify business 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 using industry expertise and emerging capabilities.
- Contribute independently within an FDE pod while mentoring newer team members.
- Coach client teams and end users on platform capabilities and AI enablement, while building trusted relationships, managing expectations, and supporting long-term engagement success.
- Drive end-to-end sales and delivery support by developing demos/POCs, contributing to proposals and orals, articulating business value, and documenting solutions for smooth client handoff and knowledge transfer.
- Strengthen team and organizational impact by mentoring other FDEs through design/code reviews and feedback, while contributing reusable components to intellectual capital.
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.
- Apply architecture decisions that balance quality, safety, latency, cost, and model risk.
- 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.
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.
- 5+ 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
- Limited immigration sponsorship may be available
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
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 $155,600-$306,800.
You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.
Prep for this interview
5 questions likely to come up at Deloitte, each matched to a line in this posting.
- 1Cloud, deployment and security
They wrote: “Experience with cloud environments (AWS, Azure, and/or Google Cloud) and common platform services (storage, compute, IAM, networking)”
How would you debug a deployment that works in staging but fails at the customer's site?
How to answer
Why they ask: Customer environments differ in ways you can't see. This tests method under pressure.
- Compare versions, config and environment variables
- Check network rules, proxies and certificates (common in enterprise)
- Look at resource limits and permissions
- Add the missing check to your install steps afterwards
Tip: List the usual suspects and how you'd check each without full access.
- 2Discovery and scoping
They wrote: “Embed with clients to identify business needs and translate high-value GenAI use cases into solutions.”
A customer says “we want AI in our claims process.” What do you ask in the first meeting?
How to answer
Why they ask: Most FDE work starts this vague. They want to hear you find the real problem before you pick a tool.
- Map the current process: who touches a claim, in what order, with which systems
- Find the slowest or most error-prone step and ask what it costs them
- Agree on one measurable goal (for example time per claim, or error rate)
- Ask what data you can access, and who signs off on security
Tip: Ask about the work, not the technology. Who does it today, how long it takes, what a mistake costs, and how they'd know it got better.
- 3Integrations and APIs
They wrote: “Experience integrating LLM solutions with enterprise systems via APIs, microservices, or event-driven architectures”
Walk me through connecting our product to a customer's internal system that has no documentation.
How to answer
Why they ask: This happens weekly in the job. They want a calm, step-by-step method.
- Find who owns the system and get read access to a test environment
- Watch real traffic or logs to learn the data shapes
- Build a thin adapter with logging around every call
- Agree on how failures get reported, and who fixes what
Tip: Talk about finding the owner of the system first. The code is the easy part.
- 4Data and SQL
They wrote: “Data engineering experience with Spark, Airflow/dbt, streaming, data modeling or ML/data science background feature engineering, experimentation or model evaluation”
The customer's data is spread across five systems that disagree. How do you get one clean view?
How to answer
Why they ask: Real deployments rarely have one source of truth. They want a practical approach, not a perfect one.
- List each system and what it's the best source for
- Pick matching keys and decide how to handle records that don't match
- Load into one place with clear rules, and keep the raw copies
- Show the customer the mismatches; they often fix the source
Tip: Name the system the business trusts most for each field, and get the customer to agree.
- 5LLMs, RAG and agents
They wrote: “Build AI-enabled solutions, agentic platforms, and workflows across enterprise AI platforms.”
When would you use an agent that takes actions, and how do you keep it safe?
How to answer
Why they ask: Agent work is growing fast in FDE postings. Senior candidates are expected to know where agents fail.
- Use agents for multi-step tasks with clear success checks; avoid them where one wrong action is costly
- Give the agent narrow tools and permissions, not broad access
- Require human approval for risky actions
- Log every step so the customer can audit it
Tip: Start from the cost of a wrong action. Limit what the agent can do, and log everything.
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