Global AI Prompting Engineer PGTR

Develop, refine, and maintain prompt libraries for various transformer units/factories. Implement advanced techniques like prompt chaining, context injection, and few-shot learning for enterprise-grade accuracy. Own prompt lifecycle management including versioning, traceability, and governance. Evaluate LLM responses for precision, compliance, and bias mitigation. Conduct A/B testing and maintain performance dashboards for prompt effectiveness. Work closely with Module Owners, Subject Matter Experts, and Business Stakeholders to embed prompts into ACE workflows. Create clear documentation for prompt strategies and maintain best practices aligned with Hitachi Energy’s AI Ethics Charter. Stay updated on emerging GenAI technologies, frameworks, and tools to enhance ACE capabilities. Implement and recommend best practices for prompting across different ACE modules.

Be part of a global digital transformation initiative. Work on cutting-edge GenAI solutions. Collaborative culture with opportunities for career growth and innovation.

• Bachelor or master degree in electrical engineering

• Experience working in the transformer industry, with international standards and customer documentation.

• Strong problem-solving and analytical thinking.

• Strong linguistic and logical reasoning for crafting precise prompts.

• Strong understanding of NLP, context management, and token optimization.

• Ability to experiment and iterate to optimize outputs.

• Ability to understand business processes and translate them into ACE-specific prompts.

• Hands-on experience with LLMs and prompt engineering techniques.

• Strong interest in AI enterprise-grade solutions.

• Willingness to learn Python and experience with AI APIs services.

• Ability to work in Agile environments and manage multiple stakeholders.

• Excellent communication and collaboration skills.

• Analytical mindset with attention to detail.

• Empathy for end users and curiosity for experimentation.

• Commitment to ethical AI practices and continuous learning.

• Reduction in prompt failure rate and model hallucination.

• Improvement in accuracy of outputs by modules.

• Speed and scalability of prompt deployment across ACE modules within the Transformer Business Unit.

• Compliance with AI governance and security standards.