- 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: o 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.

📌 Technology Architect - AI/GenAI-FY 27 (Bangalore East)
🏢 Infosys
📍 Bangalore East