Job Overview
Role: AI/ML Engineer Location: Chennai, Tamil Nadu, India Experience: Experienced (Academic projects, internships, or early professionals welcome) Qualification: B.E / B.Tech / M.E / M.Tech Key Skills: Classical Machine Learning, Large Language Models (LLMs), Agentic AI architectures, MLOps infrastructure
Job Description
Hitachi Energy, a prominent global technology powerhouse championing the evolution of cleaner, more sustainable energy infrastructures across global utilities, heavy industries, and manufacturing sectors, has initiated its 2026 Off Campus Recruitment Drive. The firm is actively searching for dynamic engineering professionals to assume the role of AI/ML Engineer within its advanced Operations Center, India (INOPC-PG) team operating from Chennai, Tamil Nadu, India. Placed firmly within the Transformers Business Unit, this professional engineering track focuses on modernizing industrial and engineering workflows through the application of Classical Machine Learning, Large Language Models (LLMs), Agentic AI architectures, and robust MLOps infrastructure.
Roles and Responsibilities
- End-to-End Pipeline Engineering: Build, scale, and manage end-to-end AI/ML analytical pipelines specialized for transformer design optimization, manufacturing logistics, and live power systems operations.
- GenAI Integration: Construct and embed domain-specific Generative AI platforms, LLM agents, advanced RAG frameworks, and conversational assistant interfaces to aid engineering experts.
- Agentic Workflow Setup: Support the execution of secure agentic AI pipelines where autonomous software agents plan, coordinate, and act across distributed data systems under pre-defined enterprise guardrails.
- Cloud Resource Management: Co-architect cloud-based computing architectures, storage configurations, and managed machine learning resources based on project constraints.
- Cross-Functional Co-Development: Partner closely with specialized transformer design engineers, software developers, and product heads to parse user requirements into functional ML microservices.
- MLOps Execution: Drive industrial-grade implementation of MLOps and GenAIOps frameworks, focusing on programmatic model monitoring, versioning pipelines, and automated performance fine-tuning.
- Knowledge Capitalization: Author complete technical architecture blueprints, document script workflows, and log performance evaluations to scale software reusability.
Skills and Eligibility Criteria
Educational Background: Possession of a completed Bachelor's (B.E / B.Tech) or Master's degree (M.E / M.Tech) in Computer Science, Data Science, Artificial Intelligence, Software Engineering, or an aligned technical field.
Experience: Practical, hands-on experience (gathered through deep academic projects, corporate internships, or early professional avenues) building core AI/ML data processing pipelines.
Mandatory Technical Skills:
- Python Proficiency: Mastery over Python programming including standard data science and ML libraries like scikit-learn, TensorFlow, PyTorch, NumPy, and Pandas
- Build Infrastructure: Demonstrable experience managing and configuring build agents within localized or self-hosted environments
- Generative AI & LLMs: Solid conceptual exposure to Generative AI tools, Large Language Models (LLMs), vectors, embeddings, prompt engineering methodologies, and Retrieval-Augmented Generation (RAG) environments
- Agentic AI Engineering: Conceptual understanding or prototype-level experience building agent-based multi-step AI workflows, including orchestration mechanisms, reasoning loops, and tool-calling structures
- Cloud Ecosystems: Baseline hands-on knowledge with major cloud providers (preferably Microsoft Azure environments) and their underlying AI/ML utility services
- Data Handling: Facility manipulating structured and semi-structured datasets using relational SQL engines or modern cloud storage repositories
Competencies:
- Experience using enterprise platforms like Azure ML, Azure AI Studio, Databricks, or alternative equivalents
- Familiarity navigating advanced LLM application orchestration frameworks such as Semantic Kernel or LangChain
- Familiarity with containerized application deployment, standard CI/CD tooling, or MLOps/GenAIOps lifecycles
- Basic understanding of complex industrial engineering workflows (transformer designs, electrical manufacturing, or heavy service operations)