AI Lead

12–15 years in IT Services, with a clear trajectory from technical execution to strategic leadership. 4–6 years specifically leading AI or Data Science practices, including experience with Generative AI and LLM implementation (RAG, Fine-tuning). Proficiency in Python (Back-end/AI) and React/Next.js (Front-end) to build end-to-end AI apps Ability to build a functional POC in 1–2 weeks to support the Practice Head’s 30% growth goal Deep understanding of the SDLC and MLOps, with experience moving at least 3–5 AI projects from pilot/POC to full-scale production. Documented success in managing a P&L with limited capital, demonstrating high "resourcefulness-to-revenue" ratios. Extensive experience in technical sales and RFP ownership, with a high win rate in the mid-market or boutique segment. Experience in "AI-ifying" traditional enterprise systems (e.g., migrating legacy middleware or databases to AI-enabled architectures). Exposure to multiple industries (e.g., BFSI, Retail, or Manufacturing) to identify cross-sector AI opportunities that can be "productized. Willingness to be hands-on with technical hurdles while simultaneously managing the high-level P&L. Experience converting existing software engineers (Java/Python) into AI/ML-ready developers to save on hiring. Expertise in managing the "Fail Fast" cycle of AI POCs to ensure budget isn't wasted on non-viable use cases. Expert in deploying production-ready AI using Llama, Mistral, and Hugging Face to avoid high vendor licensing costs. Proven experience building efficient Retrieval-Augmented Generation (RAG) pipelines and agentic workflows. Hands-on ability to conduct code reviews and architect data engineering pipelines using FastAPI, Pandas, and LangChain. Skilled in managing AI infrastructure across AWS, Azure, and GCP to maintain flexibility without deep partner ties. Expert in deploying production-ready AI using Llama, Mistral, and Hugging Face to avoid high vendor licensing costs. Proven experience building efficient Retrieval-Augmented Generation (RAG) pipelines and agentic workflows. Hands-on ability to conduct code reviews and architect data engineering pipelines using FastAPI, Pandas, and LangChain. Skilled in managing AI infrastructure across AWS, Azure, and GCP to maintain flexibility without deep partner ties. Ability to turn custom AI projects into repeatable solution templates to shorten sales cycles. Proven track record of leading technical discovery calls and drafting winning, value-based RFP responses. Ability to identify "Blue Ocean" niches for mid-market clients who are underserved by larger consulting firms.

Python Langchain LLM