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Mahiyadav Sidda
Junior Machine Learning Engineer specializing in LLMs, RAG, and on-device AI
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About
Built an "Offline Study Assistant" that runs LLM inference locally on a 5-year-old Android device using Llama.cpp and the Android NDK, achieving a 27x speedup and cutting time-to-first-token from 11 minutes to 30 seconds. Also has applied backend/API experience with FastAPI, Supabase (Auth + RLS), and production hardening of a RAG system at Hashmint using Celery and Redis to eliminate PDF-processing-related query failures.
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Key Strengths
- Optimized on-device LLM inference from 11 minutes to 30 seconds time-to-first-token (27x faster overall) on Snapdragon 720G
- Designing resource-constrained inference pipelines with focus on latency, memory, and battery tradeoffs
- Scaling FastAPI services with Gunicorn multi-workers and Redis caching; enabling frontend via CORS and OpenAPI
- Implementing API security controls (rate limiting, API keys) for ML endpoints
- Managing migrations/refactors with transactions, validation hooks, blue-green cutovers, and canary rollouts (10% traffic) using Supabase connection pooling
- Improved RAG system robustness by addressing PDF chunking/doc-processing bottleneck; reduced ~15% query failures using Celery + Redis queues
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