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Yash Vishe
Junior Software Engineer specializing in LLM systems, data engineering, and ML
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Backend/ML systems engineer with experience at SDSC, UCSD, and Media.net, building production semantic dataset/model discovery using embeddings + Solr KNN and LLM-based intent/reranking at 5M+ dataset scale. Emphasizes offline/online separation for predictable serving, has delivered measurable gains (23% retrieval accuracy, 38% latency reduction) and helped secure a $3M+ NSF grant.
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Key Strengths
- Designed production semantic discovery backend for 5M+ datasets using offline semantic indexing + fast query-time retrieval
- Improved semantic retrieval accuracy by 23%
- Architecture tradeoff expertise: front-loaded offline compute to avoid expensive runtime LLM calls and stabilize p99 latency
- Built scalable FastAPI APIs with strict schemas, stateless handlers, async jobs, caching/pagination, and autoscaling behind load balancers
- Led incremental data pipeline refactor/migration with parallel runs, automated data quality checks, idempotent DAGs, and feature-flagged cutover
- Delivered 38% latency improvement in SQL/data infrastructure refactor (PySpark/Airflow/Redshift)
- Implemented production auth patterns: JWT, scoped permissions, and database-enforced row-level security
- Proactively identified LLM inference failure mode (long-prompt bursts causing queue buildup) and mitigated via request limits and logging
- Contributed to securing $3M+ NSF grant via production-grade system outcomes
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Experience
Education
Awards
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Publications
5 publications
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