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Abhijay Sai Paladugu
Junior Data Scientist specializing in LLM agents, RAG, and reinforcement learning
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About
McKinsey practitioner who built and deployed production LLM systems for consultants/clients, including a Power BI-integrated multi-agent chatbot (RAG + text-to-SQL + formatting) with custom Python orchestration, verification loops, and a 100+ case eval set achieving ~95% consistency. Also delivered a taxonomy-mapper agent that standardized inconsistent labeling for C-suite stakeholders, cutting a process from >2 weeks to <30 minutes through demos and business-focused communication.
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Key Strengths
- Built and deployed a Power BI-integrated multi-agent LLM chatbot in production at McKinsey
- Designed hybrid agent architecture (RAG + text-to-SQL + dialogue/formatter) with deterministic routing and strict schemas
- Implemented verification loops and execution-based checks to reduce hallucinations in high-stakes client decision contexts
- Created and used a 100+ example curated evaluation set; achieved ~95% response consistency in eval and client tests
- Hands-on orchestration experience (LangChain/LangGraph) plus custom Python orchestration for enterprise constraints
- Strong measurement mindset: tracks accuracy, Pass@k, task success rate, SQL execution accuracy, latency, and cost
- Effective collaboration with non-technical stakeholders; reframed technical work into business value and secured buy-in
- Delivered major process improvement via taxonomy mapper: reduced standardization workflow from >2 weeks to <30 minutes
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2 publications
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