AI Automation Engineer

• 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.

Your Background

• 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, privac