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Sravani Kasaraneni
Mid-level Machine Learning Engineer specializing in NLP and cloud MLOps
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About
Built and deployed a production LLM-powered internal documentation assistant using embeddings, a vector database, and a RAG pipeline to reduce time spent searching PDFs/manuals. Experienced in orchestrating end-to-end LLM workflows with Airflow/LangChain, improving reliability via monitoring/error handling, and driving measurable quality through retrieval and hallucination-focused evaluation metrics.
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Key Strengths
- Built and deployed a production RAG assistant for internal document search
- Strong document preprocessing (cleaning/chunking) to improve retrieval quality
- Hallucination reduction via strict prompting and grounding responses in retrieved context
- End-to-end pipeline design and orchestration (ingestion → chunking → LLM inference)
- Reliability engineering: ordering, error handling, and monitoring of workflows
- Structured agent/workflow design with component-level testing (retrieval/reasoning/response)
- Evaluation-driven iteration using metrics (chunk hit rate, correctness, latency, hallucination rate, success rate)
- Effective collaboration with non-technical stakeholders via prototypes and iterative feedback
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