Siddharth Mathur

Architected webhook driven real-time event notification system for AI model training jobs, reducing ML team polling overhead by ~80% and cutting mean time-to-feedback on training milestones from hours to seconds. Designed and implemented AI inference API with Redis token-bucket rate limiter, sustaining 99.99% uptime under burst AI inference traffic - mirroring production reliability standards at scale. Orchestrated the creation of automated ETL pipelines for ML model performance reporting and dataset versioning, reducing data processing time for the ML team by 20% and accelerating iteration cycles. Devised methods to transform raw inference results into structured analytical summaries, decreasing report generation time by 40% and enhancing data accessibility.

Optimized ML-adjacent analytical SQL queries, reducing execution time by 60% through index tuning, query rewrites, and execution plan analysis on large-scale energy time-series datasets. Reduced AI report-generation API latency by 50% by migrating synchronous pipelines to async processing with parallel Excel output rendering in Spring Boot. Devised Python data quality layer with Pandas/NumPy, automating outlier detection for 1.2 M daily rows and trimming preprocessing time by 75%, enabling faster ML dashboard refreshes. Refactored React.js frontend for data-heavy ML dashboards, achieving 15% build-size reduction and faster load times for real-time model output visualisations.

Deployed an image preprocessing pipeline that reduced media storage costs by 30% and decreased CDN bandwidth consumption by 18%. Corrected 12 high-priority defects in the supply-chain management system, directly contributing to a 5% improvement in overall system uptime. Incorporated UI/UX specifications from the design team, elevating user experience and interface aesthetics, contributing to a 5% increase in user engagement.

Spearheaded the creation of a RAG chat system, applying document chunking and embedding generation, which reduced AI response latency by 20% for end-users. Devised prompt engineering and chain of thought reasoning frameworks that boosted LLM answer relevance by 38%, while custom retrieval logic cut data leakage incidents by four per month. Accelerated portfolio analytics by connecting quantum computing APIs, processing and exposing analytical outputs via Pandas/NumPy pipelines, reducing executive reporting time by 35%.