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GOWRI SHANKAR ANANTHULA
Mid-level Data Scientist & Generative AI Engineer specializing in LLMs and RAG
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ML/NLP practitioner who built a retrieval-augmented generation (RAG) system for large financial and operational document sets using Sentence-Transformers (all-mpnet-base-v2) and a vector DB (e.g., Pinecone), with a strong focus on retrieval evaluation and chunking strategy optimization. Experienced in entity resolution (rules + embedding similarity with type-specific thresholds) and in productionizing scalable Python data workflows using Airflow/Dagster and Spark.
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Key Strengths
- Built and iterated a RAG system over financial/operational documents to reduce manual review time
- Strong retrieval-quality optimization (chunking/overlap tuning; precision/recall evaluation)
- Pragmatic entity resolution approach combining rules + embeddings with validation-driven iteration
- Improves semantic search relevance using vector DBs and domain fine-tuning while managing overfitting risk
- Production-grade Python workflow design (modular ETL, orchestration, logging/monitoring, version control)
- Scales pipelines with distributed compute (Spark), partitioning, parallelization, checkpoints, and bottleneck monitoring
- Clear prototype-to-production decision-making based on stability across segments, cost, and operational readiness
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