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Varshith Dupati
Mid-level Software Engineer specializing in AWS, full-stack development, and AI data systems
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Backend engineer who built a Python-based data profiling/statistics platform processing up to 50M rows and ~300 metrics, using a DAG execution model, multithreading, and smart caching to cut processing time by up to 70%. Also improved PostgreSQL query performance from 12s to 2s via indexing/query rewrites, integrated an LLM (LangChain + OpenAI) for explainable “chat with the pipeline” functionality, and designed an AWS EC2+SQS architecture for scalable, isolated per-user processing.
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Key Strengths
- Designed DAG-based statistical computation engine to eliminate recomputation
- Performance optimization at scale (50M rows, 300 metrics) with measured gains (50% faster at 100k; 70% faster at 50M)
- Strong caching strategy design (key-value cache; intermediate result reuse)
- Database performance tuning (indexing + query rewrite) reducing query latency from 12s to 2s
- Integrated LLM into backend with access to intermediate DAG node context for explainability/traceability
- Built AWS queue-based worker architecture (EC2 + SQS) for scalable, isolated per-user processing
- Solved cache explosion via custom LFU/LRU-like eviction enabling scaling from ~1M to 50M+ rows
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