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Lahari Chamarthi
Mid-level Data Scientist specializing in NLP, recommender systems, and ML deployment
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About
At Provenbase, built and shipped a production LLM-powered semantic search and candidate matching platform (RAG with GPT-4/Gemini, multi-agent orchestration, Elasticsearch vector search) to scale sourcing across 10M+ candidate records and 1000+ data sources. Drove sub-second performance, cut LLM spend 30% with routing/caching, and improved recruiting outcomes (+45% sourcing accuracy; +38% visibility of underrepresented talent) through bias-aware ranking and tight collaboration with recruiting stakeholders.
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Key Strengths
- Built and deployed production LLM semantic search + candidate matching across 10M+ candidate records and 1000+ data sources
- Designed RAG architecture using GPT-4/Gemini with multi-agent orchestration and Elasticsearch vector retrieval
- Achieved sub-second retrieval performance at scale
- Reduced LLM costs by 30% via routing and caching
- Improved output reliability with JSON-schema structured prompting, programmatic validation, retries, and fallbacks
- Established rigorous evaluation approach (unit tests per step, offline metrics, A/B testing/shadow deployments)
- Delivered measurable recruiting impact: +45% sourcing accuracy and +38% visibility of underrepresented talent
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