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Sayak Banerjee
Intern Machine Learning Engineer specializing in LLMs, RAG, and search systems
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Built and shipped production improvements to a Paylocity RAG-based AI assistant, redesigning retrieval into a hybrid HNSW + keyword pipeline and using tuned RRF to fuse rankings—cutting latency by ~2s and reducing token usage by ~5000. Previously spearheaded Apache Airflow integration across ETL pipelines at Acuity Knowledge Partners, creating reusable templates and automated triggers to reduce manual job monitoring.
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Key Strengths
- Improved production RAG assistant by replacing sequential retrieval with hybrid HNSW + keyword pipeline
- Reduced chatbot latency by ~2 seconds and token usage by ~5000 via retrieval changes and prompt tuning
- Implemented tuned Reciprocal Rank Fusion (RRF) to fuse dense and keyword rankings for Top-N retrieval
- Scaled orchestration by integrating Apache Airflow across existing ETL pipelines
- Built template-based ETL integration enabling time-based and event-based triggers, reducing manual monitoring
- Strong evaluation discipline for agents/RAG (unit tests, mocked tool outputs, end-to-end metrics, RAGAs)
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