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Bhavana Polakala
Intern Data Scientist specializing in GenAI agents, RAG, and ML platforms
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About
LLM/agent systems builder who deployed a production hybrid router for immerso.ai that dynamically selects retrieval vs reasoning vs generative pathways, achieving an 82% factual-accuracy lift. Deep hands-on experience optimizing local Mistral 7B inference (4–5 bit GGUF quantization, KV-cache reuse) and building reliable RAG/agent workflows with LangChain/LangGraph/AutoGen across GCP Cloud Run and AWS (ECS/Lambda).
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Key Strengths
- Built and deployed production LLM system (hybrid router) on GCP Cloud Run
- Improved factual accuracy by 82% via routing, retrieval grounding, and self-critique loops
- Strong performance optimization for local LLMs (4-5 bit quantization, KV-cache reuse, batching)
- Effective context/prompt optimization using dynamic retrieval and compact context windows
- Designs reliable agent workflows with modular steps, guardrails, and confidence checks
- Uses golden datasets and regression suites to systematically reduce hallucinations and instability
- Hands-on orchestration of multi-agent workflows (LangGraph, AutoGen) and RAG pipelines (LangChain)
- Translates qualitative stakeholder feedback into measurable metrics and scoring rubrics
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