• Bachelor’s (B.E / B.Tech) or Master’s degree in Computer Science, Data Science, Artificial Intelligence, Software Engineering, or a related field.
• Hands‑on experience (academic, internship, or early professional) building AI/ML pipelines.
• Strong experience managing and configuring build agents (including self-hosted environments)
• Proficiency in Python, including ML and data libraries (e.g., scikit‑learn, TensorFlow/PyTorch, NumPy, Pandas).
• Exposure to Generative AI concepts, including large language models (LLMs), embeddings, prompt engineering, or RAG-based systems.
• Familiarity with agent‑based or multi‑step AI workflows (e.g., tool calling, orchestration, reasoning loops) at a conceptual or prototype level.
• Basic experience with cloud platforms (preferably Microsoft Azure) and AI/ML services.
• Experience working with structured and semi‑structured data using SQL databases and/or cloud storage.
• Understanding of core ML concepts, model evaluation, and responsible AI practices.
• Ability to collaborate effectively in a team-oriented, co‑development setup with engineers and domain experts.
• Strong analytical skills, clear communication, and a willingness to learn new technologies.

Preferred

• Experience with Azure ML, Azure AI Studio, Databricks, or similar platforms.
• Familiarity with LLM frameworks (e.g., Semantic Kernel, LangChain, or equivalents).
• Exposure to CI/CD, containerization, or MLOps/GenAIOps concepts.
• Basic understanding of engineering or industrial systems workflows (design, manufacturing, operations, or service).

• Develop and support end‑to‑end AI/ML pipelines for transformer design, operations, manufacturing, and service-related workflows, including data ingestion, model training, evaluation, and deployment.
• Assist in architecting cloud-based AI/ML and GenAI resources (compute, storage, AI services) based on solution requirements and use cases.
• Build and integrate Generative AI solutions such as LLM-based assistants, retrieval‑augmented generation (RAG) systems, and domain‑specific chatbots for engineering and operational users.
• Support the development of agentic AI workflows, where AI agents can reason, plan, and coordinate tasks across tools, data sources, and systems under defined guardrails.
• Co‑develop AI solutions in a collaborative, cross‑functional environment, working closely with transformer design engineers, operations teams, and digital product stakeholders.
• Help translate engineering and operational requirements into technical solution designs and modular AI architectures.
• Support the implementation of MLOps and GenAIOps practices, including automation, versioning, monitoring, and lifecycle management.
• Contribute to testing, validation, and performance tuning of AI/ML and GenAI solutions prior to deployment.
• Document solution designs, AI architectures, and pipeline workflows to support scalability and reuse across functions.
• Participate in design reviews, technical discussions, and continuous learning initiatives related to applied AI in the transformer domain.

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