AI Automation Engineer

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.

Qualified individuals with a disability may request a reasonable accommodation if you are unable or limited in your ability to use or access the Hitachi Energy career site as a result of your disability. You may request reasonable accommodations by completing a general inquiry form on our website. Please include your contact information and specific details about your required accommodation to support you during the job application process.

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As part of our recruitment process, Hitachi Energy uses digital and automated tools, including Al-supported solutions, to assist with activities such as application screening, job matching, and interview scheduling. These tools are designed to support our recruiters and do not replace human decision-making. Candidate data is processed in accordance with applicable data protection and employment laws as well as Hitachi's Global Data Privacy Notice. Background Screening and Security Checks

As part of the hiring process, Hitachi Energy conducts pre-employment background checks that may include verification of employment history, education, criminal records, and other relevant information, in accordance with applicable laws.

For certain roles—particularly those involving access to sensitive information, financial responsibilities, client data, regulated environments, or security-sensitive functions—additional or more comprehensive background or security screenings may be required. These may include, but are not limited to, enhanced criminal history checks, credit history reviews (where legally permissible), sanctions screening, or other due diligence measures aligned with the responsibilities of the position.

The scope and depth of any background or security review will be determined based on the nature of the role and business necessity, and will always be conducted in compliance with applicable federal, state, and local laws. Candidates will be notified and, where required, asked to provide consent prior to the initiation of any such checks.