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Tanmayee Chandanam
Mid-level Data Scientist specializing in recommender systems, NLP, and real-time ML pipelines
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About
AI/LLM engineer who built and productionized an internal RAG-based knowledge system that ingests diverse sources (PDFs, Markdown, Slack), scaled retrieval with distributed FAISS and parallel ingestion, and reduced hallucinations via re-ranking, grounding prompts, and post-generation validation. Also has hands-on orchestration experience with Airflow and Kubernetes for reliable ETL/model pipelines, monitoring, and staged rollouts; reports ~15% accuracy improvement and adoption as the primary internal knowledge tool.
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Key Strengths
- Built and deployed an end-to-end internal RAG knowledge system (data pipelines through production monitoring)
- Handled heterogeneous document ingestion (PDF/Markdown/Slack) via custom parsing, chunking optimization, and deduplication
- Scaled vector retrieval by moving from single-node FAISS to distributed FAISS and parallel ingestion
- Reduced hallucinations using retrieval quality improvements (chunking, cross-encoder re-ranking, metadata filtering) plus grounding prompts and post-generation sentence support checks
- Production reliability practices with Airflow + Kubernetes (retries/backoff, dependency isolation, auto-restarts, monitoring/alerts, staging validation)
- Delivered measurable impact: reduced engineer search time and improved accuracy by ~15%; became primary internal knowledge tool
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