• Design and develop enterprise-grade Generative AI solutions using Python and up-to-date AI frameworks.
• Build AI applications using LLMs, embeddings, prompts, function calling, tool usage, and agentic AI patterns.
• Implement Retrieval-Augmented Generation (RAG) pipelines for enterprise knowledge retrieval and generation use cases.
• Design end-to-end AI solutions covering:
• Data ingestion o Data processing o Retrieval o Generation o Evaluation
• Work with Agentic AI frameworks such as CrewAI, LangChain, LangGraph, LlamaIndex, AutoGen, and Langfuse.
• Apply prompt engineering strategies to improve LLM accuracy, reliability, and usability.




• Evaluate the suitability of fine-tuning vs RAG based on business and technical requirements.
• Support LLM fine-tuning workflows including instruction tuning and supervised fine-tuning.
• Manage data preparation for fine-tuning, including train/validation splits, data quality checks, and contamination prevention.
• Deploy AI workloads on cloud platforms such as Azure, AWS, or GCP.
• Containerize AI applications using Docker and work with basic Kubernetes concepts.
• Apply model engineering best practices for scalable, reliable, and production-ready AI systems.

📌 Senior Technologist (Bengaluru)
🏢 Infosys
📍 Bengaluru