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Akshay Amudhan
Entry Machine Learning Engineer specializing in quantitative finance and DeFi
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About
Built and deployed a production RAG chatbot using a vector database + LangChain-orchestrated pipeline, focusing on grounded, context-aware responses. Demonstrates practical trade-off thinking (retrieval quality vs latency/cost), hallucination control, and iterative improvement through logging, manual review, and stakeholder feedback loops.
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Key Strengths
- Built and deployed a production RAG chatbot end-to-end (ingestion → embedding/indexing → retrieval → LLM generation)
- Balances retrieval quality vs latency via chunk-size tuning, retrieval-depth limits, and constraining LLM context
- Iterative post-deployment optimization using latency checks and manual answer review
- Designs modular, testable AI workflows with clear stage boundaries to improve debuggability and reliability
- Uses logging/tracing of inputs, retrieved context, and outputs to diagnose failures and improve performance
- Effectively translates stakeholder needs into usable outputs and iterates based on non-technical feedback
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