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. How We Build 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. The Role 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 at 9pm. This 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. What You Will Do 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 goes wrong at 11pm, because something will. Compounding the platform: Identify what is reusable and push it back into the core. Resist the pull toward one-off solutions that don't compound. Every deployment should make the next one faster. Technical Challenges You Will Own • Safe execution boundaries between deterministic and agentic logic • Stateful, long-running case workflows that survive restarts, retries, and partial failures • Document understanding at production quality across messy, real-world inputs • Integrations with ERPs, core banking systems, document and policy stores, ticketing systems • Making LLM-based systems observable, governable, and debuggable in production • Approval flows, escalation logic, and human-in-the-loop where it actually belongs • An engineering environment where coding agents are part of the default development model What You Need • Strong backend engineering background: Python, Go, or TypeScript, at least one at depth; you have designed distributed systems, APIs, and workflow-based architectures • Production instincts: idempotency, retries, logging, monitoring, on-call, incident handling • Hands-on with LLM-based systems and tool-based agent architectures or a clear track record of ramping fast on new infrastructure • Comfort operating client-facing inside a live engagement, in messy environments with imperfect requirements • Bonus if you have worked on workflow engines, document understanding or case management systems, ERP or core banking integrations, or in regulated industries • Fluent German and English: this is a client-facing role in DACH Who This Is For It is not a fit for support or implementation-consultant profiles, platform engineers who want to stay insulated from clients, research backgrounds without shipping track record, or anyone who needs requirements to arrive cleanly defined. We are early. This is Aufbauarbeit: deliberate, funded, and already in production, but Aufbauarbeit. Stack Python, FastAPI, Postgres, Celery, and Go on the backend. TypeScript and React on the front. Claude via Vertex AI in EU (Frankfurt) for the LLM layer. Coding agents are part of the default development workflow. Practicalities Düsseldorf is our center of gravity; Berlin works with regular presence at clients in NRW. Hybrid by default, with regular on-site time at clients across DACH. Competitive base plus bonus. Start as soon as your notice period allows. Apply via this posting or reach out to me directly - I read every application myself.