Forward Deployed Engineer (AI/GenAI)

Most AI engineers build demos. You’ll build the production system a client’s business actually depends on.

Team: AI Delivery Practice

Location: Remote (Ukraine/Europe)

Employment: Full-time

Reports to: Head of AI Delivery/Engineering, not Sales, not Customer Success

Travel: Typically 20–30%, concentrated around engagement kickoffs and go-live weeks, confirmed with you per engagement, not a surprise

The Job, Honestly

Most AI engineering jobs let you perfect a demo. This one doesn’t. You get one client, one real opportunity, and six to twelve weeks to turn it into something running on live traffic — in their environment, on their data, with their security team watching. Then you do it again, for a different client, in a different industry, with a different set of constraints.

Success here isn’t measured in closed tickets. It’s measured in production adoption, business impact, and whether the client can run what you built without you standing over their shoulder.

If that sounds like the fun part of the job rather than the exhausting part, keep reading.

The AI Practice

You won’t be the only person figuring this out. Delivery patterns each engineer builds — a guardrail design, an eval harness, a way of scoping a RAG project so it doesn’t die in week 6 — get written down and reused by the next person on the next engagement. You’re not starting from zero each time, and neither is the person after you.

Engagements span the kind of variety a single-product company can’t offer: a RAG system on a client’s live data one quarter, an agent with real write-access and real guardrails the next, a legacy enterprise stack under regulatory constraints after that.

What the Work Actually Looks Like

Discovery through production — not discovery through a slide deck

• You sit with the client’s team — their repos, their data, their stand-ups — and find the AI opportunity worth betting six weeks on, not the one that looks best in a pitch

• You analyze their business processes, data landscape, and existing systems well enough to translate a business problem into a production-ready AI architecture

• You prototype it, validate it with real end users, and iterate on measurable outcomes before you’ve convinced yourself it’s right

• You ship it — and you’re still the one on call when it breaks in week two of production, not someone who inherits your code

• You capture what you learned so the next engagement, and the next engineer, start a little further ahead than you did

Before the contract is even signed

• Sales brings you into scoping calls, because “can this actually ship in 6–12 weeks” is an engineering judgment, not a sales one — and what you say shapes what gets sold

The actual engineering, once you’re in

• Production LLM applications — agents, RAG, orchestration — built on whatever stack the client’s environment actually runs on, not the stack that’s most fun to write in

• Integrations into whatever the client already has: their APIs, identity provider, cloud, messaging systems, the legacy piece nobody wants to touch

• Reliability work that never shows up in a demo: tool-call guardrails, retries, fallbacks, human-in-the-loop checkpoints

• Eval pipelines, golden datasets, and observability — because the job isn’t done at go-live, it’s done when the client can run the thing on their own

• The unglamorous optimization work: latency, cost, security, maintainability — whatever the client’s actual bottleneck turns out to be

Who Tends to Thrive Here

Less a checklist, more a description of the person who’s already done a version of this job:

• You can talk in detail about a production AI system you shipped for an external customer — what they wanted at kickoff, what they wanted at launch, and why that changed. This matters more to us than any line on your CV

• 5+ years as a commercial software engineer, with strong fundamentals and the ability to pick up whatever stack a given engagement requires — we hire for engineering depth and adaptability, not a fixed programming language

• Fluent with at least one modern LLM platform (OpenAI, Anthropic, Gemini, Azure OpenAI, Bedrock), and comfortable in the RAG/vector/embeddings/context-engineering space without needing a primer

• You’ve built agents with something like LangGraph, LangChain, Semantic Kernel, CrewAI, or AutoGen — and you’ve watched one misbehave in production, which taught you more than the framework docs did

• You can ship and run things on AWS/Azure/GCP with Docker, Kubernetes, and CI/CD — and you understand enough about IAM, auth, and networking to not be the reason the client’s security team says no

• You can hold your own in a room with a skeptical enterprise stakeholder who’s been burned by an AI vendor before — not by having all the answers, but by being straight about trade-offs

• Upper-Intermediate English or better, because a lot of this job is a conversation, not a pull request

Extra credit, not a requirement

• You’ve built eval frameworks, golden datasets, or regression tests for AI systems, or worked with LangSmith, Langfuse, Arize Phoenix, or MLflow

• You know your way around AI governance, guardrails, hallucination mitigation, or prompt injection — or MCP / A2A and where AI interoperability is heading

• You’ve touched Databricks, Azure AI Foundry, Bedrock, or Vertex AI at the platform level, not just the API level

• You’ve done consulting, solution architecture, or technical pre-sales before — or worked inside a regulated industry (healthcare, finance, telecom, government) and know why that changes everything

• You have a GitHub history, a conference talk, or a client-co-authored case study that shows this rather than just claiming it

Your First 90 Days

• Weeks 1–2: Embedded in your first client’s environment, with a validated, highest-value use case identified jointly with their team — not handed to you

• Weeks 3–8: Architecture designed, prototype validated with real end users, production build underway

• Weeks 9–12: System live on real traffic, eval pipeline in place, documentation and handover ready for the client’s own team to take over

What We Offer

• Competitive compensation with bonuses tied to successful production go-lives; B2B contract available

• Direct visibility into your impact — your code runs at the client, not sitting in a backlog

• No two engagements are the same: a fintech RAG project this quarter, a regulated-industry agent build next

• Certifications (Anthropic, OpenAI, AWS, GCP, Azure) and a professional development budget

• Health insurance, English classes, sports activities, and a culture that tells you the hard parts of the job up front — like we just did

If the six-to-twelve-week cycle, the client-hopping, and the “you’re still holding the pager” ownership sound energizing rather than draining — this is probably your role. Send us your CV, and tell us about the production AI system you’re proudest of shipping.