About
I’m an AI Engineer at GSK focused on building production-grade AI systems that solve real enterprise problems. My work spans Agentic AI, Multi-Agent Systems, Generative AI, LLM applications, RAG, semantic search, and AI-powered automation, using Python, FastAPI, Azure OpenAI, Azure AI Search, LangChain, LangGraph, and Databricks. At GSK, I’ve worked on AI systems operating across 1M+ enterprise assets, including multi-agent chatbots, RAG-based semantic search, agentic data-quality automation, Business Glossary generation, metadata-driven AI workflows, and enterprise API integrations. My work has delivered measurable outcomes including 70–80% reduction in manual rule-authoring effort, 99.6% reduction in workflow latency, 50% improvement in search relevance, 500+ engineering hours saved annually, approximately £920K in annual savings, and a 90% reduction in production AI failures. I’m particularly interested in building reliable AI systems that go beyond simple LLM integrations by combining agents, retrieval, tools, APIs, evaluation, observability, and enterprise data into scalable production solutions. Core areas include Agentic AI, Multi-Agent Systems, Generative AI, LLMs, RAG, Prompt Engineering, LangChain, LangGraph, Azure OpenAI, Azure AI Search, FastAPI, Python, Databricks, Data Engineering, AI Evaluation, and LLMOps.
Work experience
AI Engineer at GSK, Aug 2024 - Present · 2 yrs 2 mos, Bengaluru · Hybrid, • Developed and deployed enterprise-grade AI solutions focusing on Agentic AI, Generative AI, and intelligent search at GSK. • Architected an Agentic AI workflow that reduced manual effort by 80% and cut workflow latency by 99.6%. • Engineered a production Multi-Agent AI chatbot, improving search relevance by 50% across 1M+ data assets.
Graduate Intern at GSK, Jan 2024 - Aug 2024 · 8 mos, Bengaluru · Hybrid
