AI Engineer

Are you curious about AI and ready to move beyond research - building robust, production-ready solutions that tackle real challenges in Climate Tech? At Enjins, we bring a Software Engineering mindset to everything from traditional ML models to modern LLMs and Agentic architectures. We value freedom to work with the latest technologies while creating genuine, measurable business value.

**About us 🚀
**Since 2018, we've been engineering AI systems designed to deliver long-term, meaningful value. Operating from Utrecht and Berlin, we partner with innovative tech companies across Northwest Europe - including Groendus, SnappCar, Next Sense, Crisp and Beyond Weather - and actively prioritise partnerships in Climate Tech where our technology delivers measurable positive impact.

We build data & AI solutions. At Enjins, we operate with a product mindset, meaning we take full ownership of the technical lifecycle - from initial discovery to a scaling, production-grade engine that lives within our partners' infrastructure. At our core, we are engineers first: clean code, MLOps best practices, and system reliability. Our team is a growth-oriented group of engineers from diverse cultural, academic, and professional backgrounds.

What? [You'll be doing] 👀
You will act as a hands-on engineer embedded within client teams, designing and deploying end-to-end AI/ML systems from proof-of-concept to production. Technical mastery meets high-impact client engagement.
**
Key Responsibilities**

• Own a feature or deployment pipeline on client projects, demonstrating technical mastery and end-to-end responsibility.
• Spearhead the adoption of MLOps/LLMOps best practices - feature stores, model monitoring, pipeline orchestration — to ensure client systems are scalable and reliable.
• Collaborate with Data Scientists and Software Engineers to design and implement robust data pipelines and model APIs.
• Bridge the gap between data science and business value by translating technical findings into clear, actionable insights for non-technical stakeholders

What success looks like (within 6 Months) 🎯
A concrete example of what this could mean in practice: within your first months, you might build a predictive energy-demand model for a Climate Tech client - going from raw sensor data all the way to a monitored, production API. That's the kind of real-world impact we're after.