The opportunity
Perform the hands-on experimental work that shows whether promising AI technologies hold up under realistic conditions. As AI Experimentation Engineer in the Technology Validation team, part of Hitachi Energy's Data, Analytics & Artificial Intelligence (DAAI) organization, you turn selected AI opportunities into feasibility experiments, comparative evaluations, demonstrators and technical spikes. Your evidence gives architecture, platform and delivery teams what they need before they commit to a technology. The team builds to learn and demonstrate, not to operate production systems.
How you will make an impact
- Design and run time-boxed experiments and technical spikes against clear learning questions, testing first the assumption most likely to change the conclusion.
- Build prototypes, test harnesses, reference implementations and integrations, proportionate to the question and under sufficiently realistic conditions.
- Conduct fair comparative evaluations of AI models, LLMs, services, frameworks and vendor options, using a baseline or alternative where it strengthens the conclusion.
- Create early demonstrators that make new AI capabilities tangible for stakeholders, even before a business owner has been identified.
- Document method, results and constraints concisely. Maintain reproducible, reusable technical assets.
- Collaborate with the AI Research Engineer and AI Opportunity Lead. Provide evidence, demonstrations and constraints to DAAI Architecture, AI Platform Operations, AI Acceleration and AI Delivery without taking on delivery or product ownership.
Our benefit offering is tailored based on your country of employment and generally includes:
- Competitive salary and incentives
- Well-being and care
- Paid and unpaid leave
- Flexible work options
- Benefits that support your financial security
- A genuine passion for AI: you try new tools and models on your own initiative and can show a recent example of something you built or tried.
- 3+ years of experience in AI/ML experimentation, prototyping, software engineering, data science or applied AI.
- Strong hands-on programming skills (Python preferred, other languages such as JavaScript or TypeScript welcome) and the ability to build working prototypes quickly, using AI coding assistants where useful. Familiarity with cloud AI platforms (Azure preferred, GCP or AWS) is an advantage.
- Experience evaluating LLMs, ML models, AI services or integrations. You can run a time-boxed experiment, turn the findings into a decision and stop when more work would not change it.
- Intellectual honesty and clear communication: you report negative results as clearly as positive ones and explain limits to non-technical audiences.
- Bachelor's degree or equivalent practical experience and fluent English. A degree in computer science, data science or engineering is an advantage.
