--- title: "Aditya Verma - Consultant - GSK, US in Bengaluru | Tal" description: "Meet Aditya Verma, Data Scientist @ IQVIA | GenAI & LLM Engineer – Agentic AI, Multi-Agent Systems, RAG, LLM Evaluation | LangGraph · LangChain · MCP · FastAPI · Azure OpenAI | IIT Madras based in Bengaluru. AI stack includes claude code, gemini, and github copilot." image: "https://tal.club/meet/aditya-verma-12/share-image" --- # Aditya Verma on Tal ## Headline Data Scientist @ IQVIA \| GenAI & LLM Engineer – Agentic AI, Multi\-Agent Systems, RAG, LLM Evaluation \| LangGraph · LangChain · MCP · FastAPI · Azure OpenAI \| IIT Madras ## About I'm an AI Engineer at IQVIA building production GenAI systems for life sciences and pharma: agentic workflows, RAG pipelines, MCP tooling and LLM evaluation that hold up in front of real users\. My working principle: LLMs reason, code computes\. Models interpret evidence; deterministic code handles the calculations\. Every output has to be grounded, traceable and testable\. WHAT I'VE BUILT • Agentic drug\-launch forecasting: LangGraph multi\-agent workflows combining research agents, validated citations and deterministic forecast models\. • Forecast monitoring: MCP\-based agents that assess how approvals, trial results and policy changes affect forecast assumptions\. • SHAP Assistant \(Explainable AI\): turns SHAP attributions into plain\-English clinical insights, with validator guardrails against unsupported explanations\. Cut analysis time from hours to under 50 seconds \(\~95%\), 75% user adoption, 200\+ daily queries\. • Document intelligence: a custom FastAPI MCP server that turns unstructured PDFs into agent\-ready data, enabling autonomous financial\-audit workflows with \~90% less manual oversight\. • FusionSync: multimodal RAG on a unified FAISS vector store with async orchestration and server\-side caching\. 60% lower memory overhead, 75% faster downstream processing\. • Demand forecasting: statistical, machine learning and deep learning models for pharmaceutical demand planning\. HOW I WORK Grounding, evaluation, traceability and failure handling come first, then cost and latency trade\-offs\. LLM evaluation is my current deep\-dive\. Outside work, I implemented a GPT\-2\-style transformer in PyTorch from scratch, including training, text generation and fine\-tuning\. CORE STACK Python · SQL · LangGraph · LangChain · MCP · Claude Agent SDK · FastAPI · Azure OpenAI · RAG · FAISS · Qdrant · Neo4j · PostgreSQL · Cosmos DB · Docker · PyTorch · scikit\-learn · SHAP RECOGNITION • M\.Tech, IIT Madras: highest CGPA in my class of 2023 • Gold: IQVIA GenAI Hackathon • B\.E\., Savitribai Phule Pune University: Department Rank 5 OPEN TO AI Engineer, GenAI / LLM Engineer, Machine Learning Engineer and Applied AI roles focused on agentic systems and LLM infrastructure\. 📩 verma\.aditya1294@gmail\.com GitHub: github\.com/jskaditya1294 Portfolio: portfolio\-site\-tawny\-rho\.vercel\.app ## Location Bengaluru ## AI stack claude code gemini github copilot langsmith mcp ChatGPT ## Work experience Consultant \- GSK, US at IQVIA, Sep 2026 \- Present · 2 mos, Remote Associate Consultant at IQVIA, May 2026 \- Aug 2026 · 4 mos, Bengaluru, SHAP Assistant — Explainable AI Platform \(GenAI \| FastAPI \| Azure OpenAI\) Built a production\-grade Explainable AI platform that translates complex SHAP feature attributions into plain\-English clinical insights — reducing manual analysis time by 95% \(from hours to under 50 seconds\)\. Key engineering decisions: • Designed a RAG\-inspired pipeline that fuses SHAP outputs with high\-dimensional clinical data • Implemented validator\-based guardrails to ensure 100% adherence to SHAP\-derived evidence, eliminating hallucinations in automated medical insights • Integrated Azure Cosmos DB \+ Blob Storage for persistent state and explainer caching • Architected a session\-aware Dash UI supporting multi\-turn reasoning • Achieved 75% user adoption and handles 200\+ daily queries at IQVIA Stack: Python · FastAPI · Azure OpenAI · SHAP · Dash · Cosmos DB · Blob Storage · Docker Impact: Solved the "black\-box" problem for clinical AI — giving stakeholders trust in automated insights for the first time\. Analyst \(Advanced Analytics\) at IQVIA, Sep 2024 \- Apr 2026 · 1 yr 8 mos, Bengaluru, Context\-Aware DI — MCP\-Based Financial Auditing Pipeline Architected an MCP \(Model Context Protocol\) pipeline that transforms unstructured PDFs into queryable, agent\-ready assets — enabling autonomous AI agents to perform real\-time financial auditing with 90% less manual oversight\. Key engineering decisions: • Built a custom MCP Server using FastAPI that bridges enterprise document data with LLMs • Designed for scalability: any unstructured PDF becomes a structured data source accessible to LLM agents • Enabled autonomous validation workflows — agents can audit financial documents end\-to\-end without human intervention Stack: Python · FastAPI · MCP · LangChain · PDF parsing · LLM agents Impact: Dramatically reduced manual audit effort in enterprise financial workflows — a direct application of emerging agentic AI infrastructure\. FusionSync — Multimodal RAG Infrastructure Engineered a multimodal RAG system that synchronises unstructured text and media data into a unified FAISS vector store, purpose\-built for agentic workflows\. Key engineering decisions: • Built an asynchronous backend orchestration layer with server\-side caching to handle heavy embedding maps • Reduced browser memory overhead by 60% through optimised embedding management • Boosted processing efficiency by 75% for downstream agentic workflows Stack: Python · LangChain · FAISS · LangGraph · Async Python · Vector databases Impact: Built the retrieval backbone enabling agents to reason over heterogeneous data sources — text, images, and documents — in a single unified pipeline\. ## Education Master of Technology \(M\.Tech\.\), Indian Institute of Technology, Madras Bachelor of Engineering \- BE, Civil Engineering, Savitribai Phule Pune University [View the original profile](https://tal.club/meet/aditya-verma-12) ```json { "@context": "https://schema.org", "@graph": [ { "@type": "Person", "@id": "https://tal.club/meet/aditya-verma-12#person", "name": "Aditya Verma", "url": "https://tal.club/meet/aditya-verma-12", "image": "https://tal.club/meet/aditya-verma-12/profile-photo.jpg", "description": "Meet Aditya Verma, Data Scientist @ IQVIA | GenAI & LLM Engineer – Agentic AI, Multi-Agent Systems, RAG, LLM Evaluation | LangGraph · LangChain · MCP · FastAPI · Azure OpenAI | IIT Madras based in Bengaluru. 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