Solution AI Lead

Engineering is human. So are you. AI is a means to a better outcome, not the point.

At Devleaps, we help clients go from 'we tried a chatbot' to 'we have a reliable AI capability embedded in our systems'. We're looking for an AI Tech Lead who starts from the business problem, and who can tell a CTO plainly which AI investments will pay off this year and which will not.

WHAT YOU'LL DO

• Lead technical direction on AI engagements: own the architecture and translate it into a delivery plan with sequencing, cost per use case, and risk attached.
• Serve as the trusted technical voice for clients: challenge assumptions about what AI can realistically do for their roadmap, and set expectations that survive contact with reality.
• Shape a pragmatic AI adoption path: identify the two or three places where AI will move the needle this quarter, park the rest explicitly, and say plainly when a rules engine is the better investment.
• Make the business case defensible: cost per resolved case, latency against SLA, and quality metrics tied to the actual outcome rather than model accuracy alone.
• Build the platform as a means, not an end: treat shared guardrails, evaluation and prompt management as the thing that makes use case two cost a fraction of use case one.
• Own the technical narrative: run workshops that turn a vague wish to use AI into a scoped, costed, de-risked plan a CTO can sign off on.
• Remove what slows teams down: find the systemic bottleneck, whether it is data readiness, governance, or inflated AI expectations from stakeholders, and fix the actual constraint.

YOU ARE

• A seasoned engineer who has led: you have made architectural calls and owned outcomes, and you know the difference between a good demo and a good idea that ships.
• A consultant at heart: you ask before you prescribe, and you are just as comfortable saying AI is not the answer here as you are proposing it.
• Comfortable with ambiguity and politics: competing stakeholder interests, legacy constraints, and inflated AI expectations do not slow you down.
• Outcome minded: you measure success by client impact, not by how much AI made it into the solution.
• Human, practical, and allergic to hype decks.

YOU'LL BRING

• 6–10+ years of software engineering experience, with a few years in a lead or principal capacity, and hands-on delivery of LLM or ML systems in production.
• A track record of shipping AI capabilities that were adopted and kept running — not demoed once — with a cost and evaluation story attached.
• Solid command of AI system architecture: RAG design, agentic patterns, and a defensible view of when a well-tuned prompt or a plain rules engine beats either.
• Strong Python and hands-on experience with LLM APIs and orchestration frameworks, plus a clear view of when to use which.
• Working knowledge of vector databases, semantic retrieval, and evaluation frameworks — enough to size and de-risk a build.
• Deep understanding of distributed systems and architectural trade-offs, and the ability to explain them in business terms.
• Working knowledge of where AI genuinely reduces cost or time to market, based on real delivery rather than vendor claims.
• Hands-on experience with at least one major cloud platform (preferably Azure).
• Consultancy instincts: comfortable scoping, costing, and defending an AI programme to a client stakeholder or procurement.
• Comfortable operating from junior engineer to VP of Engineering.
• Fluent Dutch, professional English.