Virtasant is a global technology services company with a network of over 4,000 technology professionals across 130+ countries. We specialize in cloud architecture, infrastructure, migration, and optimization, helping enterprises scale efficiently while maintaining cost control.
Our clients range from Fortune 500 companies to fast-growing startups, relying on us to build high-performance infrastructure, optimize cloud environments, and enable continuous delivery at scale.
A leading US healthcare workforce company has built an analytics and predictive proof of concept on Palantir Foundry. Client reaction has been strong, and the platform is now moving to a full production build that will be taken to market. We are standing up a pod of five to six engineers to deliver it, working alongside the client’s product and business teams and training their internal engineers as we build. This is forward deployed work in the original sense: you embed with the client, you own an outcome, and you ship. Beyond the first build, the client plans to place small AI pods inside its business units, each pairing an FDE with an AI engineer and a software engineer, and each accountable for a measurable business result. Do the first engagement well and there is a long runway.
What you will do Take a Palantir Foundry proof of concept to production: data pipelines, ontology, application layer, governance, observability. Sit with business and product teams, run discovery, and turn policies and operating procedures into working code and agent logic. Define an adoption or delivery metric with the client’s business leader, own it, and report against it. Train client engineers as you build, so capability stays with the client rather than with the vendor. Shape the roadmap from proof of concept to a production-grade product the client can sell.
FDE-grade is a specific bar. We gate on four signals:
1. Platform depth. Production work on Palantir Foundry or a comparable data and AI platform. Certifications alone do not qualify; we reference-check against shipped systems.
2. Customer-facing comfort. You can run a discovery session, lead a workshop, train a team, and defend a recommendation in front of a CTO.
3. Generalist build instinct. You write the glue code, the integration, the pipeline, and the dashboard. You do not wait for tickets.
4. Outcome ownership. You have shipped end to end and are comfortable being measured on adoption, time to production, or a business KPI. The fourth signal has a corollary the client cares about deeply: business context.
A great FDE comes up to speed on the business layer fast, because an engineer who only listens and codes will faithfully rebuild the inefficient process that already exists. We look for people who ask why the process works the way it does before writing a line of it.
-6+ years in software, data, or ML engineering, with senior-level ownership of production systems.
-Hands-on Palantir Foundry experience (Pipeline Builder, Ontology, Workshop; AIP a strong plus), or deep experience on a comparable platform such as Databricks or Snowflake with evidence of fast platform ramp.
-Production Python and SQL. TypeScript or Java a plus.
-Experience building with LLMs and agents: retrieval, tool use, evaluation, and getting them past the demo stage.
-Consulting, solutions engineering, or client-embedded delivery experience. Clear written and spoken English, comfortable presenting to executives
Healthcare, staffing and workforce, or other regulated-industry domain experience.
Forward Deployed Engineer, AI & Analytics 3 of 4 A track record of capability transfer: training, pairing, playbooks delivered as part of an engagement.
A prior FDE, solutions architect, or solutions engineer role at a platform vendor or AI-native company.
Recruiter interview (30 min)
Technical Interview (30 min)
Client Interview 1 (60 min)
We strive to move efficiently from step to step to make the recruitment process as fast as possible.
Contract engagement through the Gigster network, full-time dedication expected.
Initial term aligned to the production build, with extension likely as the pod model expands across business units.
Compensation is competitive and set by experience and location
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