Forward Deployed Engineer

I left McKinsey in March to build again.

After years of business building and GenAI work in regulated mid‑market environments, one thing became clear: the bottleneck in AI is not capability. It is turning capability into reliable systems that operate inside real businesses. Most companies experiment with AI. Very few build systems that are governed, production‑grade, and embedded in operational workflows.

NXT closes that gap.

We are an AI Operations company: we build and run AI systems for regulated mid‑market companies and PE portfolios in DACH. Not consulting: production systems with operational accountability, deployed into finance, back office, and operational workflows, creating measurable value in weeks.

Five months in, this is no longer a thesis. We have anchor clients in banking and healthcare, seven‑figure revenue in year one, a team of seven, and ISO 27001 certification underway – fully bootstrapped, funded by client revenue from day one.

To deploy these systems into client environments and make them work under real constraints, I am looking for a Forward Deployed Engineer.

We do not build the platform in isolation. We build it inside real, paying client engagements in regulated industries (currently banking and healthcare) and we own the reusable IP that emerges. The first engagements are the birthplaces of the first platform components. This is a deliberate choice. The platform runs in production from day one, in regulated environments, with real operational data and real consequences.

The Forward Deployed Engineer sits exactly at the seam where the platform meets the client. The engagement is the engineering environment. Deployment is where the product is forged.

This is a senior engineering role embedded directly inside client engagements. You take a workflow design and turn it into a running, governed system inside a real operational environment (banking, insurance, healthcare) with executive sponsors watching and audit requirements in the room. You work shoulder‑to‑shoulder with the founder, the platform team, and the client’s operations and IT teams, and you own the deployment from the architecture decisions down to the production incident s is not support and not configuration.

It is controlled deployment of AI systems under real constraints, where the gap between "it works in the demo" and "it runs the process" is where the value sits. The first proof point is concrete: a system you deployed end‑to‑end into a regulated environment, running real cases in production, with a client who would take the call from the next prospect.

Deployment into production:
Translate workflow designs into running systems on our platform. Adapt the architecture to the client’s real environment: core systems, document stores, shared inboxes, legacy workflows. Own the integration surface: APIs, file drops, ERP connectors, identity, data flows. Stand the system up in staging, harden it, and take it live.

Safe execution at the agentic/deterministic boundary:
Decide which decisions an LLM is allowed to make and which sit on rules, validations, or human approval. Build in the controls that make the system auditable and explainable to a regulator or an internal control function.

Operating inside the client:
Sit inside the client’s reality: desks, ops floors, IT meetings, steering committees. Earn the trust of the operator who works the cases and of the executive who signed the SOW. Close the loop between operational truth and system design, fast.

Reliability and governance in production:
Own observability, logging, retries, idempotency, failure handling. Build the on‑call and incident posture for a regulated environment. Make the system debuggable when something…