ML Engineer - Generative AI

stakeholders, and integrating AI features seamlessly into Horizon workflows. Stay up to date with advances in generative AI, agent-based systems, and instruction-tuned models, experimenting with frameworks like LangChain and collaborating on experiments using large instruction-tuned models. Collaborate closely with product and software teams to take AI solutions from concept to production, ensuring features are robust, scalable, and directly support Horizon users in monitoring, optimising, and managing renewable energy assets. What Makes This Role Exciting You'll take cutting-edge LLM and agent-based systems out of the lab and into production, building AI that directly supports the operation and optimisation of renewable energy assets at global scale. What We Offer Great atmosphere of working together with professionals and some of the most engaged and knowledgeable people in the industry. Guidance from colleagues through coaching, mentoring and participation in international networks. Opportunities to advance your professional skills and technical expertise through individual competence development plans and tailored training. Being part of a world-growing and renowned organisation with origins dating back to 1864. Benefits Medical Scheme Commuting Allowance Life Insurance Pension Plan Kindergarten Allowance 40 hours per week with a flexible schedule (Friday) Home working allowance (up to 2 days per week) 23 days of annual leave Employee referral scheme Requirements Master's degree or equivalent experience in Mathematics, Data Science, Computer Science, Machine Learning, or a related field. Hands-on experience with Large Language Models (LLMs) or generative AI systems. Experience with RAG pipelines, embeddings, and prompt optimisation. Strong understanding of Transformer architectures and fine-tuning. Proficiency in Python. Familiarity with CI/CD, Docker, Kubernetes (K8s), and Git is a plus. Fluency in written and spoken English. You are curious, adaptable, and proactive, thriving in collaborative, fast-moving environments. You take ownership of projects, communicate complex AI concepts clearly to technical and non-technical stakeholders, and enjoy solving challenging real-world problems. As part of the interview process, we ask you to submit a short report or demo of an AI agent you have built. This is your chance to showcase your hands-on skills and highlight your creativity. Security and compliance with statutory requirements in the countries in which we operate is essential for DNV. Background checks will be conducted on all final candidates as part of the offer process, in accordance with applicable country-specific l

About 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 LLM 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 (operational data, structured metadata, technical documentation) and design Retrieval-Augmented Generation (RAG) pipelines for scalable AI systems. Extract, transform, and structure data for optimal use with LLMs, manage embeddings, indexing, and context retrieval, and ensure AI models are grounded in accurate, real-world information. Design, deploy, fine-tune, and continuously improve production-grade LLM and multimodal agents, optimizing prompts, mitigating hallucinations, and evaluating performance to deliver reliable, context-aware outputs. Build and maintain client-facing AI assistants and chatbots, explaining system behavior, capabilities, and limitations to technical and non-technical