AI Solution AI Lead Hoofddorp · hybrid

Devleaps; Hoofddorp; Hybrid; EUR 120.000 - 180.000; Vollzeit

Zusammenfassung

Devleaps is seeking an AI Tech Lead in the Netherlands to own AI architecture, draft delivery plans, and guide client projects from problem to practical deployment.

You will ensure capabilities are costed, de-risked, and aligned with business outcomes while steering workshops and governance to reduce bottlenecks and unrealistic expectations. Fluent Dutch and professional English are required.

Qualifikationen

• 6–10+ years of software engineering with 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—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.

Aufgaben

• 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.

Kenntnisse

LLM/ML systems delivery

Architectural leadership

Client-focused advisory

Ambiguity management

Business/technical communication

Tools

Python

LLM APIs

Orchestration frameworks

Vector databases

Jobbeschreibung

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.