ml engineer for renewable energy asset monitoring

DNV is an independent expert in assurance and risk management. Its Energy Systems division helps customers navigate the transition to a decarbonized and sustainable energy future by assuring that energy systems operate safely and effectively through increasingly digital solutions. Digital and Data Solutions develops software and data-driven solutions for challenges in energy, infrastructure, and sustainability.

• Design and implement a scalable architecture for role-based AI agents supporting asset management, asset ownership, O&M, predictive maintenance, trading, and analytical workflows;
• Define agent responsibilities, tools, interaction patterns, task delegation, context sharing, and hand-offs between specialised agents;
• Integrate agents with Horizon data sources, including operational time-series data, alarms, events, asset metadata, analytical results, forecasts, reports, logbooks, and technical documentation;
• Build evaluation frameworks and representative test datasets covering answer quality, groundedness, tool selection, workflow completion, hallucination risk, safety, regression, latency, and operational reliability;
• Implement observability and traceability across agent execution, including prompts, retrieved context, tool calls, model responses, decisions, failures, and user feedback;
• Deploy and operate AI services in production with software and platform engineers;
• Collaborate with renewable energy domain experts to ensure agent outputs are technically meaningful, evidence-based, and appropriate for operational decision-making;
• Stay informed about developments in LLMs, agent orchestration, multimodal systems, evaluation, and AI engineering, and assess them for production use.

• Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Mathematics, or a related field, or equivalent practical experience;
• Proven experience building AI agents, agentic workflows, or AI assistants, preferably in production environments;
• Experience with Retrieval-Augmented Generation (RAG), embeddings, vector search, and prompt engineering;
• Strong Python software engineering skills and experience developing production-grade applications;
• Experience integrating AI solutions with APIs, databases, and enterprise systems;
• Familiarity with Git, CI/CD, Docker, and cloud-native development practices;
• Strong communication skills and fluency in written and spoken English;
• Curiosity, adaptability, and proactive collaboration in fast-moving environments;
• Ownership of projects and ability to communicate complex AI concepts clearly to technical and non-technical stakeholders;
• A short report or demo of an AI agent built by the candidate is required as part of the interview process;
• Final candidates must undergo background checks in accordance with applicable country-specific laws and practices;
• Nice to have: experience deploying AI workloads on Kubernetes, experience with self-hosted LLMs and model serving, familiarity with ClickHouse, MongoDB, vector databases, and time-series data platforms.

• Medical Scheme;
• Commuting Allowance;
• Life Insurance;
• Pension Plan;
• Kindergarten Allowance;
• 40 Hours per week with a flexible schedule;
• Home working allowance up to 2 days per week;
• 23 Days of annual leave;
• Employee Referral scheme;
• Coaching, mentoring, international networks, individual competence development plans, and tailored training.