Forward Deployed Engineer Job Richardson area,Texas USA,Software Development

We are hiring a Forward Deployed Engineer (FDE), an elite, hands‑on engineer who embeds with our business partners and turns their hardest operational problems into working, adopted software. You will lead short, high‑intensity engagements (often two to four weeks from blank page to working prototype), validating hypotheses with real users and iterating in near‑real time. You own the full arc — discovery, scoping, build, production, and adoption — at the same seniority level as our Principal Full‑Stack Engineer role.
Right now, the program slate centers on an agentic transformation of the end‑to‑end occupier process: rebuilding how serves its occupier clients so that AI agents carry the work across the full lifecycle, with people supervising the decisions that matter. You would join that program and ship into it from the first engagement.
You will build primarily on Palantir Foundry (Ontology, AIP, AIP Logic, Pipeline Builder, Workshop), taking an AI‑first approach to building software. This is a hands‑on delivery role, not an advisory one: you write the code, deploy it, and stay accountable until it is in production and in use.

Responsibilities Required Skills

Embed with business SMEs to learn their workflows, data, and constraints. Become enough of a domain expert to make decisions without waiting for a spec.
Define the success measure before naming the solution: establish the baseline, set the target, and track performance against it.
Prototype and iterate. Build the smallest version that confirms or disproves a hypothesis, put it in front of users, and adjust based on real usage.
Own end‑to‑end delivery from discovery through production: architecture, build, deployment, monitoring, and handoff.
Build LLM and agentic workflows where they solve a real problem; default to deterministic, testable engineering wherever structured logic suffices.
Write clear scopes of work and lightweight delivery plans for proof‑of‑concept and production phases.
Manage scope diplomatically rather than refusing outright. Make work visible, surface dependencies and blockers, and build consensus on priorities.
Design for security, quality, and cost from the start, rather than addressing them at the end.

TechnicalAI & Generative AI

Excellent communication — earns trust as an embedded advisor and explains trade-offs clearly to technical and business audiences alike.
Bias to action — work iteratively with users toward the right solution rather than delivering the complete solution all at once.
Measure‑first mindset — you can point to past projects where you set the target up front and held the work to it.
Influence without formal authority — tactical, relationship‑preserving scoping; consensus through visibility of work, dependencies, and trade‑offs.
Comfort in a dynamic environment with evolving objectives and frequent iteration with non‑technical users.
Ownership mindset — accountable for outcomes and adoption, not just delivery.
Prior forward‑deployed, embedded‑consulting, or early‑startup‑engineer experience. You've built a product from scratch under ambiguity. Hands‑on solutions architects and professional‑services / implementation engineers are also strong fits, provided they wrote and deployed the code, owned adoption, and weren't the pre‑sales or advisory‑only version of those roles.
6+ years of hands‑on software engineering, shipping production systems end‑to‑end as a senior individual contributor.
Hands‑on Palantir Foundry experience across data engineering (Pipeline Builder, ontology modeling, transforms) and application/AI development (AIP, AIP Logic, Workshop, Functions). Certified Palantir Foundry engineer a plus.
Deploy, monitor, and debug your own builds on cloud‑native infrastructure (AWS and/or Azure), with infrastructure‑as‑code and sound operational practice.
Strong SQL and data modeling; comfortable working with large‑scale, imperfect, real‑world data.
Practical experience building solutions with LLMs and an expert understanding of the current Gen AI landscape.
Experience taking Gen AI workflows to production: prompt design, retrieval‑augmented generation, embeddings, agentic flows, and evaluation/guardrails.
Machine‑learning fundamentals: evaluation, problem decomposition, and sound judgment on when a probabilistic approach is warranted.
Design and orchestrate multi‑agent systems, with human‑in‑the‑loop interfaces that keep people in control of the decisions that matter.
Integrate models, tools, and data through open standards such as MCP (Model Context Protocol), grounded in a well‑modeled Ontology and reliable data pipelines.

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