ML Engineer – Generative AI

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.

As a trusted voice for many of the world’s most successful organizations, we use our knowledge to advance safety and performance, set industry benchmarks, and inspire and invent solutions to tackle global transformations.

About Energy Systems

We help customers navigate the complex transition to a decarbonized 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 to navigate the many complex, interrelated transitions taking place globally and regionally, in the energy industry.

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 ML Engineer to help create, productize and evolve generative AI solutions on Horizon, GreenPowerMonitor’s cloud platform that transforms renewable energy operational data into actionable insights for monitoring, optimization, 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 data, structured metadata, and 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 both technical and non-technical 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, optimizing, 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 optimization of renewable energy assets at global scale.

What we offer

Our benefits package is specifically designed to support your physical, financial and social well-being:

• Great atmosphere of working together with professionals and some of the most engaged and knowledgeable people in the industry,
• Receive guidance from colleagues through coaching, mentoring and participating in international networks,
• Advance your professional skills and technical expertise, through individual competence development plans and tailored training,
• Be part of a world growing and renowned organization with origins dating back to 1864.

Other Than You Can Expect

• 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.

DNV is an Equal Opportunity Employer and gives consideration for employment to qualified applicants without regard to gender, religion, race, national or ethnic origin, cultural background, social group, disability, sexual orientation, gender identity, marital status, age or political opinion. Diversity is fundamental to our culture and we invite you to be part of this diversity.

Requirements

About you

• 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 optimization,
• 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’ve 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 laws and practices.