Forward deployed engineers who build inside your cloud
Senior engineers who sit with your users, work in your infrastructure and ship production systems there, with no product of their own to sell you.
By the Ryz Labs team · Updated October 2026
A forward deployed engineer (FDE) is a software engineer who works directly inside a customer's organization, close to the people who will use what gets built, and ships production software in that customer's environment. Ryz Labs provides forward-deployed teams of senior engineers who build AI systems inside your cloud and repos, on US hours. Our teams have shipped fraud detection, compliance review and voice systems that run in production today.
Where the term comes from
"Forward deployed" is borrowed from military language, where it describes units positioned close to the front line rather than at headquarters. In software, the job title is most closely associated with Palantir, which built its customer work around engineers who were embedded with client teams instead of handing software over from a distance. In the last few years, as companies try to get AI from demo into daily operations, the role has spread well beyond that origin, and many AI companies now hire FDEs of their own.
The common idea is simple: the hard part of enterprise software is rarely the algorithm. It's the messy data, the legacy integrations, the security review and the workflow nobody wrote down. You solve those by putting engineers where the problem is.
What a forward deployed engineer actually does
- Learns the workflow first-hand. FDEs sit with the analysts, agents or operators who will use the system, and watch how the work happens today before writing code.
- Connects to real data. They work with your data warehouse, your document stores and your internal APIs, under your access controls, instead of a cleaned-up sample.
- Builds and deploys in your environment. Code goes into your repos and runs in your cloud, through your CI and your change-management process.
- Closes the loop with users. They ship small, watch how people use it, and iterate quickly, often within the same week.
- Owns production. Evaluation, monitoring, alerting and on-call aren't someone else's problem. An FDE stays accountable for the system running, not just for the demo.
That mix of skills is why FDEs are hard to hire. The role needs a strong production engineer who is also comfortable in a room with business users.
Signs you need forward deployed engineers
- You have an AI pilot that impressed people in a demo but hasn't been connected to real systems or real users.
- The problem depends on your own data and workflows, so no off-the-shelf product fits well.
- Your engineering team is committed elsewhere and can't spare people to own a new system.
- Security, compliance or data-residency rules mean the system has to run in your own environment.
Two kinds of FDE
It's worth separating two very different setups that share the same title:
- Vendor FDEs work for a software or model company and deploy that company's product inside your organization. They're useful if you've already bought the product, but their job is to make that product succeed.
- Independent FDE teams build on whatever stack you already run. They have no platform to sell you, so the architecture decision, including which models and clouds to use, stays yours.
Ryz FDE teams are the second kind. We work with AWS, Azure, GitHub, Postgres, and Anthropic and OpenAI models, and the system we build belongs to you.
FDEs vs. consultants vs. staff augmentation
| Forward deployed engineers | Consultants | Staff augmentation | |
|---|---|---|---|
| Main output | Production software, running in your environment | Recommendations, roadmaps, sometimes a prototype | Added capacity on your existing team |
| Who sets the technical direction | The FDE team, with your engineering leaders | The consultants advise; you decide later | Your engineering managers |
| Time with end users | High; it's central to the role | Interviews and workshops | Depends on the role |
| Accountable for | The system working in production | The quality of the advice | Their share of the team's work |
| Best for | Getting a specific AI system from idea to production | Strategy, operating model and transformation programs | Teams that already know what to build and need hands |
| With Ryz | AI pod teams. Custom quote, scoped per team | Not offered | Staff augmentation. Custom quote, scoped per team |
How Ryz forward-deployed teams work
Our FDEs work as AI pod teams: dedicated groups of senior engineers, typically about seven people including a tech lead, an ML engineer and backend engineers, working as one team in your time zone.
Inside your cloud, under your rules
The pod gets access through your identity provider, with the permissions your security team approves. Data stays in your accounts. Models are called through your own provider agreements. Nothing you depend on runs in a Ryz environment you can't see.
Alongside your team
The pod joins your repos, CI and standups, and runs weekly demos of working software. Your engineers review the code and can pair with the pod, so knowledge builds up on your side as the system takes shape.
Built to hand over
When the system is stable, you choose: keep the pod on the next problem, add people or disciplines, or hand the whole system to your own engineers. Because everything already lives in your repos and cloud, handover is about context, not migration.
What our forward-deployed teams have shipped
- Fraud detection for a global fleet company. More than 244K invoices scored, under 30 seconds per invoice, and $5.94M in fraud confirmed, with results validated by the client's own fraud team.
- Marketing compliance for a global capital management firm. 8,000+ documents reviewed, with turnaround cut from days to hours.
- An AI voice platform that has placed more than 1M outbound calls.
- A driver-support agent handling 218K conversations a year in three languages.
Each of these is the kind of system FDEs exist for: specific to one company's data and workflow, and only valuable once it runs every day. More in our case studies.
Who we work with
We're trusted by Fortune 500 engineering teams and serve enterprises across industries, including financial services, logistics, real estate and software. Our engineers are based in Latin America, work within an hour of US time zones, and come through a vetting process that only the top 1% of candidates pass. You sign one contract with Ryz. Our engineers work with us as independent contractors, and we handle paying them.
When FDEs are not the right fit
- You want a strategy engagement or a board-level transformation program. A large consultancy is built for that.
- You want a finished AI product off the shelf. Buy it from a platform vendor, and use their FDEs to roll it out.
- You need coverage across European and Asian time zones.
Comparing providers? Read our guide to forward-deployed engineering firms and partners that take an AI pilot to production, or see how we compare with Tribe AI.
FAQ
What is a forward deployed engineer?
A software engineer who works inside a customer's organization, close to the users, and builds and runs production software in the customer's own environment.
How are FDEs different from consultants?
Consultants mostly deliver advice and plans. FDEs write the code, deploy it in your environment and stay accountable for it running in production.
Do Ryz FDEs work in our cloud or theirs?
Yours. Our teams work in your cloud accounts and repos, through your access controls, and you own everything they build.
Which models and platforms do your FDEs use?
Whatever you run. Our teams commonly work with Anthropic and OpenAI models on AWS and Azure, with GitHub and Postgres.
How is a forward-deployed team priced?
Custom quote, scoped per team. You get a scoped plan, a price and the names of the people who would do the work before you sign.
Questions we didn't answer? Email info@ryzlabs.com.
Senior engineers in your time zone. AI pod teams that ship.
Tell us what you're building. You'll get a scoped plan, a price and the names of the people who would do the work.
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