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Rudraksh Jadhav
Intern Software Engineer specializing in AI and full-stack web development
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Built ReflectlyAI, an AI-powered interview coach, implementing a low-latency Python/Flask backend with modular LLM/Whisper services, retries/fallbacks, caching/batching, and async/background processing. Demonstrates strong PostgreSQL/SQLAlchemy performance tuning (EXPLAIN ANALYZE, composite indexes, selectinload) and multi-tenant isolation patterns (tenant-scoped schemas, tenant_id middleware), reporting ~50% response-time reduction.
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Key Strengths
- Built an AI-powered interview coach backend (ReflectlyAI) using Python/Flask
- Architected modular isolation layer for LLM/Whisper to enable fast iteration and model/prompt swapping
- Performance-focused database optimization using indexes and SQLAlchemy loading strategies (e.g., selectinload)
- Uses PostgreSQL EXPLAIN ANALYZE to diagnose and validate query performance improvements
- Designed multi-tenant data isolation using tenant-scoped schemas, row-level filters, and tenant_id middleware
- Implemented reliability patterns for AI workflows (rate limiting, retries, fallback responses)
- Reduced average API response time by ~50% via caching/background processing
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