Forward-Deployed Engineer

Regen8 builds AI operating systems for mid-market companies. We build for sovereignty: the client’s workloads, their data, and the intelligence the system accumulates stay theirs. Our forward-deployed teams embed with operators, map how the work actually moves, and put the first agent into governed production in 30 days.

We work where trust shapes how work gets done: private equity and their portfolio companies, digital health, manufacturing, and construction. In these businesses, judgement and accountability are not tradeable for speed. Three rules hold on every build. Systems of record remain authoritative. Human authority remains explicit. Context, controls, and learning stay with the client.

Forward-Deployed Engineers are the client-facing builders. You embed with everyone from the CFO to the person actually doing the work. You run interviews with process owners, trace how systems really talk to each other, and build the agents with the client rather than for them.

This is a role for people who communicate clearly, think like operators, and build like engineers. You sit with a controller and map how the close actually runs, not how the SOP says it runs, then turn that into specs, prototypes, evals, and working agents.

A demo and an agent a business depends on at month-end are not the same artifact. We ship the second one. If the thought of your code touching someone’s payroll run does not make you slow down and verify, this is not your job.

The role starts on contract and converts to full-time on performance, which is when equity and bonus come with it. We would rather prove the fit on real client work than on an interview loop.

You work paired with a Applied AI Strategist , who owns the engagement and the business case. You own the architecture and the delivery.

• Embed with client teams across finance, revenue, and operations, and map the work step by step and system by system. Go past the golden path. The exceptions and the workarounds are the actual job
• Build the agents, integrations, and controls that take one operation into governed production in 30 days
• Connect to the systems already in place. No migrations. The client’s system of record stays authoritative, and you design so that stays true
• Decide with the client where a human must hold the decision, then build so that boundary cannot be crossed by accident. Every consequential action stops at a person
• Write the evals. Know whether the agent is getting better, and be able to prove it to someone who is sceptical
• Drive adoption. Train the team, sit with the users, watch what they actually do, and iterate on what you see rather than what they reported
• Hand over ownership. The client keeps the context, the controls, and the learning. Build it so they can run it without us
• Codify what works into our method, so the second build of a thing costs less than the first

Three skillsets that are rare in one person: communication, analysis, and engineering. You need to be strong in all three.

• Track record You have solved ambiguous problems for senior stakeholders and shipped the result. We care more about what you have built and owned than where you trained.
• AI-native You use Claude Code, Cursor, or an equivalent every day. You have built with language models, including prompt design, agents, and evals, and you hold an argued point of view on what works.
• Technical enough to build it Python or Type Script, comfortable with APIs, able to read a data model and follow a trace. You have shipped something real that people depended on.
• Client presence You can push back on a CFO without losing them, and move between altitudes without losing either the executive or the engineer.
• A steep learning gradient A client teaches you their workflow once and you can explain it back better than they did.
• Comfort in someone else’s stack Including the old, undocumented parts.

• Forward-deployed or implementation engineer at an AI company
• Technical founder or early employee at a company that sold into business buyers
• Solutions architect or sales engineer who built every demo themselves
• Senior engineer who wants to work closer to the customer on a compressed timeline
• Data or analytics lead who shipped a model into a real workflow and owned the outcome