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Ashwin Ram
Junior Data Scientist specializing in Generative AI and applied machine learning
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About
At Evoke Tech, built a production LLM "Testbench" to quickly compare LLMs/embedding models and RAG strategies (semantic, hybrid BM25, re-ranking, HyDE, query expansion) to select optimal architectures for different client needs. Also developed a multi-agent, multimodal (voice/text) RAG system for live catalog retrieval and safe product recommendations using LangGraph/LangChain with LangSmith monitoring, and regularly translated PM/UX goals into concrete agent behaviors via demos and flowcharts.
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Key Strengths
- Built and deployed an LLM testbench to rapidly evaluate LLM/embedding models and RAG architectures across client use cases
- Model-agnostic architecture design across multiple LLM providers using standardized classes, core APIs, and shared YAML/configs
- Hands-on orchestration with LangChain/LangGraph and monitoring via LangSmith
- Designed and implemented multi-agent RAG workflows with live tool/catal og retrieval and safety/quality guardrails
- Strong experimentation methodology: start simple (prompting/retrieval) and iterate to hybrid, re-ranking, and advanced RAG
- Collaborates effectively with PMs/UX; communicates AI workflows via interactive demos and flowcharts
- Testing discipline for agent workflows (unit tests plus manual evaluation)
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