AI Automation Engineer

The Opportunity

The Service Excellence team is part of the Common Shared Services (CSS) organization and supports multiple functions including Finance, Supply Chain Management, HR Operations, and Trade, Transport & Logistics. As an AI Engineer within the Business Process Automation team, you will be part of a dynamic team focused on enabling CSS functions to deliver services with greater efficiency and effectiveness.

This opportunity covers two complementary tracks within the same team. AI Delivery focuses on industrializing and scaling AI solutions into secure, stable, production-grade services. AI Exploration focuses on evaluating emerging AI capabilities and proving their value through rapid prototypes and pilots. Successful candidates will be aligned to the track and level that best matches their skills and experience, with opportunities to contribute across both areas over time.

How You'll Make an Impact

• Design and build AI-powered and agentic solutions that automate complex, repetitive, or decision-driven tasks across Common Shared Services processes.
• Develop applications leveraging large language models (LLMs) and multimodal foundation models using techniques such as Retrieval-Augmented Generation (RAG), tool calling, structured outputs, and context engineering.
• Build, orchestrate, and govern AI agents using frameworks such as LangGraph, Semantic Kernel, Microsoft Agent Framework, Azure AI Foundry Agent Service, and Model Context Protocol (MCP).
• Develop APIs, integrations, and event-driven pipelines that embed AI capabilities into enterprise platforms including SAP, ServiceNow, Microsoft 365, Power Platform, data lakehouse environments, and existing automation technologies.
• Optimize models through prompt engineering, fine-tuning, distillation, and quantization to achieve the right balance of accuracy, performance, scalability, and cost.
• Establish AI evaluation and observability practices including automated evaluations, regression testing, tracing, quality monitoring, cost governance, and performance measurement.
• Apply Responsible AI and security-by-design principles, including content safety controls, human oversight, prompt injection protection, privacy safeguards, auditability, and regulatory compliance.
• Partner with business stakeholders, process owners, and analysts to identify automation opportunities, assess business value, and deliver impactful AI solutions.
• Translate technical concepts into actionable business insights while maintaining clear documentation, architecture definitions, and knowledge-sharing materials.
• For AI Delivery, drive the industrialization of validated AI use cases through scalable architectures, LLMOps/MLOps practices, CI/CD automation, deployment standards, and production support models.
• For AI Exploration, evaluate emerging AI technologies, conduct proofs of concept, benchmark new capabilities, and prototype innovative approaches including GraphRAG, agentic workflows, document intelligence, and multimodal AI solutions.
• Ensure compliance with applicable external and internal regulations, procedures, and guidelines.
• Live Hitachi Energy's core values of safety and integrity by taking responsibility for your own actions while caring for your colleagues and the business.

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field. A PhD is considered an advantage for candidates aligned to the AI Exploration track.
• 5-10 years of experience in software engineering, AI engineering, machine learning, or related technical disciplines.
• Demonstrated experience delivering AI or Generative AI solutions into production environments, or experience building prototypes, conducting applied research, and evaluating emerging AI technologies.
• Strong programming capabilities in Python, including testing, packaging, asynchronous programming, and software engineering best practices. Experience with SQL is required, while Java or C#/.NET is an advantage.
• Hands-on experience with LLM and agent frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, Microsoft Agent Framework, OpenAI Agents SDK, Google ADK, CrewAI, and MCP-based integrations.
• Strong understanding of Retrieval-Augmented Generation (RAG), enterprise search architectures, embeddings, vector databases, semantic search, hybrid search, re-ranking techniques, and GraphRAG frameworks.
• Experience working with cloud AI platforms, particularly Azure AI Foundry and Azure OpenAI, along with exposure to platforms such as Google Vertex AI, Amazon Bedrock, or Databricks Mosaic AI.
• Strong knowledge of deep learning frameworks including PyTorch, Hugging Face Transformers, PEFT, ONNX Runtime, and classical machine learning techniques using scikit-learn.
• Experience with multimodal AI technologies, intelligent document processing, OCR, speech-to-text, text-to-speech, and vision-language models.
• Knowledge of modern software engineering and platform practices including Git, CI/CD, Docker, Kubernetes, REST APIs, OpenAPI, gRPC, webhooks, and event-driven architectures.
• Experience with AI evaluation and observability tools such as LangSmith, Langfuse, Ragas, MLflow, promptfoo, or Azure AI evaluation services.
• Solid understanding of data engineering concepts including Databricks, Spark, lakehouse architectures, orchestration frameworks, data quality, and data modelling practices.
• Knowledge of Responsible AI, AI governance, OWASP Top 10 for LLM applications, prompt injection mitigation, security controls, privacy protection, and model governance.
• Strong analytical and problem-solving skills with a pragmatic, value-driven approach to solving business challenges through AI.
• Excellent communication and collaboration skills, with the ability to work effectively across global, cross-functional teams and communicate technical concepts to diverse audiences.
• A growth mindset with curiosity for emerging AI technologies and the ability to distinguish between technology trends and sustainable business value.
• Experience working in Agile environments and familiarity with Azure Boards, Jira, or similar project management tools is considered a plus.
• Proficiency in spoken and written English is required.