Title: Lead AI Engineer Location: Utrecht Utrecht NL Job Description: We are AI builders Since 2018, Enjins has done one thing: ship AI that works in production. From Utrecht and Berlin, we build machine learning systems, data infrastructure, and agentic AI layers for European tech companies, with a deliberate focus on climate and energy, where we believe the biggest technological shift of our generation should meet its most critical challenge. We embed directly with the engineering teams of high-growth scale-ups like Crisp, PlanBlue, NextSense, and Groendus. We take ownership of the full technical lifecycle: from first architecture decision to a production-grade system running inside our partner's infrastructure, long after go-live. The role As Lead AI Engineer, you are the technical owner of client engagements. You architect the solution, ship it to production, and make sure it still performs a year later. Alongside the other Leads, you shape the engineering team and the way we build. In practice: - Design AI roadmaps with client leadership and own them through to production - Set and uphold Data Platform and MLOps standards across your client portfolio, so systems stay healthy after handover - Mentor engineers and weigh in on hiring, team growth, and accounts - Push our engineering practice forward, including how we use agentic coding workflows to build faster without compromising reliability. We test, document, and share what works, so the whole team levels up - Elevate our product mindset to ensure we build the right things, not just more things Why a Lead role here, not somewhere else You'll work across more stacks, clouds, and frontier frameworks in a year than most engineers see in five, with the collective knowledge of the whole team behind you, not the isolation of freelance work. And because our clients build grid software, energy forecasting, and climate infrastructure, you develop real domain expertise in energy and climate systems on top of production-grade AI engineering. In your first months you'll join a client project alongside an experienced Enjins engineer, supported by a dedicated coach. Within half a year, you own the technical roadmap of at least one engagement, mentor engineers, and have a recognised voice in our Asset strategy. Requirements What matters most: you think in systems, own outcomes end-to-end, and grow the people around you. - 5+ years building and scaling AI solutions in production environments - Deep expertise in Python, SQL, and Git; hands-on with containerisation (Docker, Kubernetes) and ETL/ELT orchestration - Mastery of at least one major cloud platform (AWS, Azure, GCP) and experience leading MLOps/LLMOps architectures - Proven experience taking LLM-based or agentic systems to production - Experience mentoring engineers, overseeing delivery, and managing client accounts - You can translate technical complexity into clear decisions for non-technical stakeholders - Fluent in English and eligible to work in the Netherlands Benefits We publish our salary ranges and keep terms straightforward. No surprises. Compensation - €5,500 - €7,250 gross per month (excl. holiday allowance, pension, bonus, equity, and AI-Leap benefits) - Annual performance bonus of 1.5 months' salary - Equity participation (VSOP) Growth - AI-Leap: our structured development programme with a dedicated certification budget and a specialised track for Leads - 1-on-1 mentoring with our founders and external coaching to close your specific gaps Team & culture - Office on the Oudegracht in Utrecht (note: not yet fully accessible for individuals with physical disabilities); hybrid setup - 28 vacation days, flexible across 3 national holidays - Monthly team events and the annual two-day Enjins Summer Summit on AI × Climate - OV-First travel policy, fully covered; meat-free catering - Apple MacBook Pro and the tools you need

• Lead engineering delivery for agentic AI capabilities for GTM stakeholders across its technology stack (Salesforce, Slack, 3rd party apps) and In-House Platforms • Design and build LLM-powered workflows, autonomous agents, multi-agent systems and AI-enhanced integrations using Agentcore, Slack, MCPs, Langchain • Define scalable enterprise AI architecture patterns including model routing, orchestration, memory management and governance strategies • Design and optimize Retrieval Augmented Generation (RAG) systems, semantic search pipelines, vector retrieval strategies and enterprise knowledge grounding frameworks • Build and maintain Apex, Lightning Web Components, Salesforce Platform Events, and Agentforce agent actions • Implement AI evaluation frameworks including prompt quality benchmarking, hallucination reduction and model performance monitoring • Mentor senior and mid-level engineers; conduct code reviews and enforce AI engineering standards • Define error handling, fallback strategies, and graceful degradation patterns for non-deterministic AI systems • Retire legacy integrations and replace them with modern, agentic, event-driven patterns • Champion security-first AI engineering: input validation, output sanitization, and prompt injection hardening • Architect and implement CI/CD pipelines, deployment automation, and platform observability • Embrace Agentic AI and LLM-powered tooling as a core part of your engineering practice • Stay current with rapidly evolving AI/ML technologies and apply them pragmatically to GTM systems • Champion automation-first thinking — if it can be agentic, make it agentic • Build internal tools and apps to streamline data access and GTM decision-making • Implement data governance and monitoring frameworks to enhance GTM system quality • Evaluate vendors and AI platforms with a strategic build vs. buy mindset • Identify and automate manual processes to increase organizational leverage

• Build and scale AI systems: Design, implement, and optimize core components of our agentic AI architecture with a focus on performance and reliability • Develop agentic workflows: Build on multi-agent systems using LLMs, orchestration frameworks (LangGraph), RAG, and context engineering • Ship advanced AI features: Develop innovative capabilities such as layout optimization, structured reasoning flows, and multimodal workflows • Contribute to product evolution: Collaborate closely with product and engineering teams to shape features through experimentation and rapid iteration • Drive continuous improvement: Help refine best practices for evaluation, deployment, monitoring, and scaling of AI systems in production

• Report directly to our VP of Agentic AI • Shape the core intelligence and behavior of the agent — how it interprets intent, plans, assists, and recovers from failure — so that it reliably helps users build working Unqork applications. • Help scale the backend that powers the agent in production, contributing to a service that stays fast, observable, and resilient as usage grows. • Drive how we measure and raise agent quality over time, turning real-world usage into a feedback loop that makes the product steadily more capable and trustworthy.