Forward Deployed Engineer

I recently left McKinsey to build again.After years working on business building and GenAI in regulated mid-market environments, one thing became clear:The real bottleneck in AI is not capability. It is turning that 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.We are building an AI-native company focused on closing that gap.Not with pilots. Not with slide decks. But by deploying governed AI systems into real operational processes — and creating measurable value in weeks.Customer operations. Claims. Servicing. Back office. Case-based workflows in regulated industries.To deploy these systems into real customer environments and make them work under real-world constraints, I am looking for aForward Deployed Engineer(Düsseldorf or Berlin)How We BuildWe do not build the platform in isolation. We build it inside real, paying client engagements in regulated industries, and we own the reusable IP that emerges. The first engagements are the birthplaces of the first platform components.This is a deliberate choice. It means the platform runs in production from day one, in regulated environments, with real operational data and real consequences. It also means it is funded by client revenue from day one.The Forward Deployed Engineer sits exactly at the seam where the platform meets the customer. The engagement is the engineering environment. Deployment is where the product is forged.What This Role Actually IsThis is a senior engineering role embedded directly inside customer engagements. You are the person who takes an Operator design and turns 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 will work shoulder-to-shoulder with the founder, the platform engineers, and the customer's operations and IT teams. You will own the deployment from architecture decisions down to the production incident at 9pm.This is not support. This is not configuration. This is controlled deployment of AI systems under real constraints, where the gap between "it works on the demo" and "it runs the process" is where the value sits.What You Will DoDeployment of AI Operators into ProductionTranslate Operator Playbooks into running systems on NXT CoreAdapt architecture to the customer's real environment — core systems, document stores, shared inboxes, legacy workflowsOwn the integration surface: APIs, file drops, ERP connectors, identity, data flowsStand up the system in staging, harden it, and take it liveSafe Execution at the Agentic / Deterministic BoundaryDesign clear boundaries between agentic proposal and deterministic executionDecide which decisions an LLM is allowed to make and which sit on rules, validations, or human approvalBuild in the controls that make the system auditable and explainable to a regulator or an internal control functionOperating Inside the CustomerSit inside the customer's reality — desks, ops floors, IT meetings, steering committeesEarn trust both with the operator who works the cases and with the executive who signed the SOWClose the loop between operational truth and system design — fastReliability and Governance in ProductionOwn observability, logging, retries, idempotency, failure handlingBuild the on-call and incident posture for a regulated environmentMake the system debuggable when something goes wrong at 11pm — because something willCompounding the PlatformIdentify what is reusable and push it back into NXT CoreHelp the platform get sharper with every engagementResist the pull toward one-off solutions that don't compoundTechnical Challenges You Will Work OnDesigning safe execution boundaries between deterministic and agentic logicBuilding stateful, long-running case workflows that survive restarts, retries, and partial failuresDocument understanding at production quality across messy, real-world inputsIntegrating with ERPs, core banking systems, document and policy stores, ticketing systemsMaking LLM-based systems observable, governable, and debuggable in productionDesigning approval flows, escalation logic, and human-in-the-loop where it actually belongsWorking inside an engineering environment where coding agents are part of the default development modelWhat You NeedStrong backend engineering background: Python, Go, or TypeScript, at least one at depthExperience designing distributed systems, APIs, and workflow-based architecturesProduction instincts: idempotency, retries, logging, monitoring, on-call, incident handlingComfort with LLM-based systems and tool-based agent architectures, or a clear track record of ramping fast on new infrastructureComfort operating in client-facing contexts where the system is being built inside a live engagementFluent German and English — this is a customer-facing role in DACHComfort with messy environments and imperfect requirementsBonus if you have worked onWorkflow engines or orchestration platformsDocument understanding or case management systemsERP, core banking, or insurance core system integrationsRegulated industries — banking, insurance, healthcareEarly-stage systems with ambiguous requirementsWhat This Role Is NotNot a support or implementation-consultant roleNot pure platform engineering insulated from customersNot a research positionNot a feature-shop job inside a stable productNot a place where requirements arrive cleanly definedWhat Success Looks LikeSuccess in this role means AI Operators that run in production at our customers — governed, observable, and trusted by the operations teams that use them and the executives that signed for them. It means engagements that go live on time, stay live, and produce measurable value. And it means the platform gets stronger with every deployment, because the reusable parts of what you build flow back into NXT Core.The first concrete proof point: an Operator you deployed end-to-end into a regulated environment, running real cases in production, with a customer who would take the call from the next prospect.Why This Role Is RareMost AI companies are still focused on model capability or demos. This role is about something harder: deploying AI systems that actually run inside real businesses, from day one, with real money and real consequences.You will help define what a Forward Deployed Engineer looks like in an AI-native operations company — not in theory, not in a blog post, but in production.StackPython, 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.StructureEarly-stage environmentHigh autonomy and direct customer ownershipDirect collaboration with the founder and the platform teamCompetitive base plus bonusDüsseldorf or Berlin, hybrid, with regular on-site time at customers across DACHIf you want to deploy AI systems that run real operations — not just prototypes — reach out directly.