AI Research Engineer

The opportunity

Bring research discipline to practical technology validation, so that Hitachi Energy can distinguish credible AI advances from hype before it invests. As AI Research Engineer in the Technology Validation team, part of Hitachi Energy's Data, Analytics & Artificial Intelligence (DAAI) organization, you interpret emerging AI research, methods, models and evaluation approaches, turn the most relevant developments into practical validation questions and answer them through prototypes, benchmarks and comparisons. Your evidence informs architecture, platform and delivery teams. The team builds to learn and demonstrate, not to operate production systems.

How you will make an impact

• Monitor and interpret AI research, publications, benchmarks and model documentation. Identify developments that merit practical testing.
• Translate research insight into concrete validation questions and fair experimental designs, with explicit baselines, success criteria and the conditions that would invalidate the hypothesis, together with the AI Experimentation Engineer and AI Opportunity Lead.
• Run research-led prototypes, benchmarks and comparisons across LLMs, RAG, agentic AI, fine-tuning and responsible-AI evaluation.
• Interpret results with methodological rigor and state the limitations clearly. Recommend a proportionate next step: stop, keep watching, test further or hand over to the team that should build it.
• Exchange methods and learnings with Hitachi Group colleagues, DAAI peers, vendors and external research communities.
• Preserve reusable methods, benchmarks and test assets so that work is not repeated.

Your background

• 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 applied AI research, AI engineering or ML research, in a company, consultancy or research institution.
• Strong programming skills (Python preferred) with hands-on AI/ML experimentation, benchmarking and evaluation. You know LLMs, generative AI, fine-tuning, RAG, agentic AI and responsible-AI evaluation. Familiarity with cloud AI platforms (Azure preferred, GCP or AWS) is an advantage.
• Ability to read research and technical claims critically and translate them into practical, enterprise-relevant experiments.
• Clear communication of research-level topics and their limitations. You have experience collaborating across research, engineering and business communities.
• Bachelor's degree or equivalent practical experience and fluent English. An MSc or PhD in AI, machine learning or computer science is an advantage when combined with practical work.