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Bharath kumar
Director-level AI & Data Science leader specializing in GenAI, LLMs, and MLOps
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About
ML/NLP engineer currently working in NYC on a system that connects complex unstructured data sources to deliver personalized insights, using embeddings + vector DB retrieval and a RAG architecture (LangChain, Pinecone/OpenSearch). Strong focus on production constraints—especially low-latency retrieval—using FAISS/ANN, PCA, index partitioning, and Redis caching, plus PEFT fine-tuning (LoRA/QLoRA) and KPI/SLA-driven promotion to production.
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Key Strengths
- Built RAG pipeline connecting multiple unstructured data sources for personalized insights
- Latency optimization for retrieval systems (embedding optimization, ANN/FAISS, index partitioning, Redis caching)
- Entity resolution pipeline combining deterministic rules with fuzzy/probabilistic matching (Levenshtein)
- Validation using sampling against verified data and precision/recall/F1 monitoring
- Production-minded ML workflows (modular design, data quality checks, CI/CD, logging/monitoring)
- Prototype-to-production discipline using SLA/KPI gates and post-launch A/B testing
- LLM fine-tuning with PEFT (LoRA/QLoRA) and quantization to reduce memory footprint
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