Principal Technologist- Q3 FY 27

This role focuses on our strategic clients who are embarking on digital initiatives. The roles require you to work closely with sales and delivery teams to drive solutions for large and complex programs, identify technology big bets, develop solution propositions around them, prototype and take them to market. This role requires to network and collaborate with CDO as well as Digital and business leaders of client organizations, develop viewpoints on industry and digital technology trends influencing enterprises.

Responsibilities

This role focuses on our strategic clients who are embarking on digital initiatives. The roles require you to work closely with sales and delivery teams to drive solutions for large and complex programs, identify technology big bets, develop solution propositions around them, prototype and take them to market. This role requires to network and collaborate with CDO as well as Digital and business leaders of client organizations, develop viewpoints on industry and digital technology trends influencing enterprises.

• Design and develop enterprise-grade Generative AI solutions using Python and modern 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.