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Ashwini Ramesh Kumar
Junior AI Software Engineer specializing in LLMs, RAG, and agent workflows
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About
Backend/ML-leaning engineer who built a content-based event recommender for FlowMingle using embeddings + HNSW vector search on Google Cloud, with Firebase as the backend and a managed recommendation lifecycle (15 recs/user, daily async generation, weekly deletion) now serving 1500+ users. Also led a cost-driven migration of ConvAI services to Azure AI using parallel request testing from a Unity client, with post-migration monitoring via logs and model evals; contributed to a Massachusetts law-enforcement conversation analysis system by expanding ingestion to PDF/TXT/Excel and multi-file inputs.
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Key Strengths
- Designed and shipped vector-search recommender (HNSW/ANN) used by 1500+ users
- Strong architectural tradeoff analysis (transformer vs vector search; cold-start, cost, inference/retraining)
- Cost/performance optimization in NoSQL/Firebase via tuning k, soft deletion, and recommendation lifecycle management
- Incremental migration strategy with parallel testing and cutover (ConvAI to Azure AI) to reduce scaling costs
- Production monitoring mindset (logs + periodic evals/analytics for model correctness and quality)
- Proactively identified real-world edge cases (multi-format, multi-file inputs) for law-enforcement conversation analysis tooling
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Publications
1 publication
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