Prepare FDEs to discover, build, and run the new work. Start with the stakeholder conversation.
The new workforce brings people and AI agents together. Forward Deployed Engineers help reimagine their shared workflows by understanding the stakeholder's problem, building against real constraints, and defining how the work will run. Nuvepro prepares those people through conversation simulations and hands-on building.
641 FDE postings · 214 distinct titles · from 264,613 real job postings · see the live data →
Practise the conversation that shapes the build.
Interview a simulated stakeholder about the work they need to improve. Surface the problem, missing information, constraints, and decisions before choosing the technology. Use the agreed problem as the starting point for hands-on building and a stakeholder demonstration.
Discover the real work
Ask how the workflow runs, where it gets stuck, and who owns the decisions. Check what data and systems are available instead of assuming the answer.
Agree what to solve
Turn the conversation into a bounded problem statement with the outcome, constraints, and handoffs that matter. Use feedback to identify what your questions missed.
Build and explain the result
Work with the scenario's data and tools in a sandbox. Demonstrate how the solution addresses the problem, explain its limits, and identify what must be checked before operational use.
These exercises prepare people for stakeholder discovery and delivery responsibilities. Human assessment, agent evaluation, and acceptance of the operational workflow remain distinct checks.
Prepare people for the FDE role →What a Forward Deployed Engineer actually is
Palantir invented the title: engineers stationed inside the customer's operation, close enough to the work to see what a demo never shows. The rest of the market has now adopted it. Sits inside the customer's workflow. Palantir-originated; Indeed reported Forward Deployed Engineer postings grew more than 700% year over year. Solutions-architect-flavored: bridges model lab and customer, deliverable is a tailored integration plus handoff.
What separates an FDE from a solutions engineer is ownership of both halves. A solutions engineer scopes and hands off. An FDE discovers the real problem, builds the fix, and stays until it runs inside the customer's systems. The deliverable is not a recommendation. It is a working change in how the work runs.
Andrew Ng, writing in The Batch, describes the job the same way the postings do: an engineer embedded in a client organization, building and tuning agentic workflows for that client's needs, where communication and business skills carry as much weight as the technical ones. Understanding needs, prioritizing projects, explaining complex technology, and pushing back respectfully when a request is unrealistic. Every one of those is a scored behavior in the discovery track below.
"I did this 25 years before the role had a name. We built India's first fully indigenous bedside monitor, and everything worked in the lab. Then I took it to hospital ERs and watched a patient roll over and flood the ECG leads with noise no test plan had imagined. The field is where the product gets finished. That is the FDE's job."
The market has already decided
Indeed Hiring Lab reports Forward Deployed Engineer postings grew more than 700% year over year. And in 2026 the hyperscalers and the model labs turned the role into a budget line.
Five commitments in a single year turned FDE from a job title into a line item. They split into three ways of funding it:
Amazon ($1B) and Microsoft Frontier ($2.5B) fund internal FDE teams from their own resources.
OpenAI's Deployment Company ($4B, TPG-led syndicate) and Anthropic's services venture ($1.5B, with Blackstone and Goldman Sachs) embed FDEs and, in doing so, bind the customer to their model.
Google Cloud's $750M fund pays system integrators like Accenture and Deloitte to deploy Gemini for customers.
Aggregate framing: Tomasz Tunguz, Theory Ventures. Each commitment independently reported (PYMNTS, TechCrunch, SiliconANGLE), April to July 2026.
"The bottleneck shifted from model capability to deployment."
That is the whole case for this page. When the constraint is deployment and not the model, the scarce resource is people who can walk into an operation and make the AI land. Every dollar above is spent on that human layer. The labs are spending it to embed their people in your operation and standardize it on their stack. The alternative is your own Forward Deployed Engineers, fluent in whatever model wins next year. That is what we build.
Standing up a $1 billion internal Forward Deployed Engineering organization, with the first postings already live.
A 6,000-person program staffed through consulting partnerships. The partners now have to produce FDEs at scale.
A publicly announced Forward Deployed Engineer force embedded with customers.
FDEs embedded with enterprise teams to leave behind patterns the teams own. HP, Intuit, Oracle, State Farm, Thermo Fisher, and Uber signed at launch.
One more thing the postings tell us: of the 10,426 tasks we extracted from FDE job descriptions, only 0.6% can be fully automated. 99% need a human in the loop or are human work outright. Companies are hiring this role precisely because it cannot be replaced by the thing it deploys.
Four capabilities. One continuous engagement.
The whole track runs inside one simulated customer. Your FDEs interview its stakeholders, prototype against its constraints, extend its product, and demo to the people they interviewed. Every stage is anchored on the customer problem, because that is what the role is anchored on.
Learn in GenAI Sandboxes
Your FDEs start inside provisioned environments seeded with the domain: the data shapes, the systems, the vocabulary of the operation they will walk into. Not slideware. Working environments where wrong answers fail visibly.
Practice in the Customer Interview Simulator
Timed conversations with simulated stakeholders who hold hidden facts about the real problem and reveal them only when trust is earned. Pitch too early and the conversation closes. A scored debrief shows what your FDE surfaced, what they missed, and which questions would have gotten there.
Prototype in sandboxes
The problem surfaced in discovery becomes the build target. Your FDEs prototype against masked data inside a sandbox that mirrors the customer constraint they discovered, including the ones IT put there for good reasons.
Extend a working product, then show the customer
The capstone. A reference product is already running, seeded with data and the same gaps real systems have. Your FDE grafts the prototype onto it without breaking what works, then demos the result back to the stakeholder they interviewed. Scored on whether it lands.
From prototype to a product that is already running
A prototype is one part of the work. The customer may run a live product with years of data in it, integrations nobody documented, and users who did not ask for a change. The integration needs its own practice, evidence, and agreed responsibilities.
So we made the graft the capstone. Your FDE inherits a running reference product, complete with the workarounds and gaps that made the customer problem exist in the first place, and extends its functionality with the prototype they built. It has to work without breaking what already works. Then they demo it to the stakeholder they interviewed, and the demo is assessed on whether it answers the problem that stakeholder revealed. This is practice in a reference environment. Production integration and acceptance are agreed separately with the workflow owner.
Questions operations leaders ask us
Should we build an internal FDE team or buy vendor FDEs?
Andrew Ng's caution in The Batch is the sharpest version of the tradeoff: vendor FDEs are there to integrate that vendor's product deeply, and in a market where nobody can predict next year's best model, binding your processes to one vendor costs you optionality. In 2026 that stopped being hypothetical. OpenAI ($4B) and Anthropic ($1.5B) each stood up separately funded deployment companies whose engineers embed in your operation and standardize it on their model, which is exactly the switching cost Ng warns about. An internal FDE team keeps the choice of AI stack yours. Both patterns are live in the market, and either way someone has to develop the people. Nuvepro enables the team you choose to build, yours or your partner's, on the same track.
What does an FDE team ship first?
Start with one defined workflow and a problem agreed with its stakeholders. Your team practises discovery, builds a solution in a sandbox, and demonstrates how it addresses that problem. The capstone can include extending a reference product. Preparation, integration and production acceptance are scoped to the work and the team's starting capability, with the timeline agreed for that engagement.
We already have solutions engineers. Is this a new hire or a conversion?
Start with the capabilities your people already bring. Solutions, customer, and implementation engineers can build on stakeholder-facing experience while adding AI-build skills. Developers may need more practice in discovery and stakeholder communication. Scope the preparation to the workflow and the gaps, rather than assuming everyone needs the same curriculum.
Why does the customer problem matter more than the tech?
Because the FDE deliverable is not a model, it is a change in how the customer's work runs. An FDE who can build but cannot surface the real problem ships the wrong thing faster. That is why discovery practice is scored as seriously as the build.
What do FDE conversations help people practise?
Understanding the stakeholder's workflow before proposing a solution. People practise asking for missing context, discovering constraints, agreeing a bounded problem statement, and explaining how their build answers it. A simulated conversation is preparation and feedback, not a customer sign-off or authorization to put an agent into production.
Prepare the people who connect discovery to delivery.
Bring a workflow, your team's starting experience, cohort levels and delivery regions. Discuss stakeholder conversation practice, hands-on building, tools, licenses, support and the readiness evidence your FDEs need.