ML Engineer – Generative AI
Pubblicato il 17-08-2026 - DNV in Milano
About Us
We are the independent expert in assurance and risk management. Driven by our purpose, to safeguard life, property and the environment, we empower our customers and their stakeholders with facts and reliable insights so that critical decisions can be made with confidence.
About Energy Systems
We help customers navigate the complex transition to a decarbonised and more sustainable energy future. We do this by assuring that energy systems work safely and effectively, using solutions that are increasingly digital. We also help industries and governments navigate the many complex, inter‑related transitions taking place globally and regionally, in the energy industry.
The Role
GreenPowerMonitor, a DNV company, is at the heart of the global energy transformation. We use data‑driven digital solutions to optimise the performance of renewable energy installations around the world. Our work contributes to a more diverse, more sustainable global energy mix.
We are looking for an ML Engineer to help create, productise and evolve generative AI solutions on Horizon,
GreenPowerMonitor’s cloud platform that transforms renewable energy operational data into actionable insights for monitoring, optimisation and decision‑making.
This is a hands‑on, impact‑driven role where you will build LLM‑powered agents and embed them directly into a production system used by renewable energy professionals worldwide. You will work closely with product managers and software engineers to design, deploy and maintain production‑grade AI systems, starting with a user‑oriented AI assistant embedded in Horizon.
Over the course of the year, your work will support the strategic goal of enriching Horizon with LLM models, starting with a predictive maintenance module and evolving the platform to become more autonomous and intelligent for users, turning operational data into actionable insights.
What You’ll Do
- Connect to diverse data sources within the Horizon platform, including operational