Vetted SQL Query Optimization Professionals

Pavan Devulapalle

Screened ReferencesModerate rec.

Mid-level Software Engineer specializing in cloud platforms and AI-integrated full-stack development

Seattle, WA3y exp

“Backend engineer who built Flask-based internal APIs supporting GenAI-driven provisioning/diagnostics (Outpost/AWS Outposts-like environment), with deep hands-on optimization across Postgres/SQLAlchemy (2s to <200ms endpoint improvement). Experienced integrating ML/LLM workflows via AWS SageMaker and Bedrock, and designing multi-tenant isolation plus high-throughput Redis-backed background task pipelines (minutes to seconds).”

“Backend engineer who built a low-latency real-time transaction API in Python/Flask, with strong depth in PostgreSQL/SQLAlchemy performance tuning (time-based partitioning, indexing, connection pooling). Has production experience integrating ML scoring and OpenAI-style APIs with safety/latency controls, and designing multi-tenant isolation strategies including per-tenant pooling/caching and premium-tenant isolation.”

Vignesh Shanmugasundaram

Screened

Junior Software Engineer specializing in full-stack development and applied ML

New York, NY2y exp

“Full-stack engineer with experience at Zoho and Amazon who has owned production systems end-to-end, including a monolith-to-microservices migration using Kafka and Cassandra that improved search latency ~25% and increased throughput without data loss. Also built a hackathon project (Buildwise) into a sold product for a construction company (AI-driven document compliance checks) and shipped an IoT-based parking availability MVP in 3 weeks.”

Tara Munjuluri

Screened

Junior Software Engineer specializing in full-stack and AI systems

Ames, IA3y exp

“Backend-focused engineer with end-to-end ownership experience on internal platforms at John Deere, including a workforce and skills system that cut manual review time by 40%. Brings a strong reliability and compliance mindset across Java/Python microservices, AWS infrastructure, and production operations, and has also built an LLM-powered RAG system over 1M+ records with emphasis on grounded outputs and observability.”

Varshitha Macherla

Screened

Junior Full-Stack Developer specializing in Java microservices and cloud platforms

Overland Park, KS2y exp

“Full-stack engineer (~2.6 years) with strong Java/Spring Boot backend experience and React/Angular frontend exposure, who has worked on enterprise-scale systems at Dell processing ~1.8M daily transactions/events. Built secure, partner/internal-facing APIs (OAuth2/JWT) across 14 integrations and implemented Kafka-based order/payment workflows with idempotency and sub-700ms processing targets, plus CI/CD and Selenium-based release validation.”

Timothy Chu

Screened

Senior Software Engineer specializing in backend systems and data pipelines

Atlanta, GA11y exp

“Backend-leaning full-stack engineer from Home Depot who operated in small, startup-like teams with end-to-end ownership of critical production systems. Stands out for combining Go/Python backend depth, React/TypeScript collaboration, and strong reliability instincts—improving search latency by 40%, cutting DB latency by 35%, and hardening high-volume data and compliance pipelines.”

“Data/analytics engineer who built and operated a DocuSign-based real-time analytics platform end-to-end, processing 20–50k webhook events/day with ~99.5% reliability. Strong in idempotent event processing, schema-evolution-safe ingestion (raw JSON + dynamic parsing), and serving data via versioned, low-latency REST APIs with solid CI/CD and observability.”

“ML/AI engineer with hands-on ownership of production document intelligence and GenAI systems, spanning model experimentation, AWS deployment, monitoring, and iterative optimization. Stands out for turning document-heavy workflows into reliable, near real-time products with measurable gains in accuracy, latency, and manual-effort reduction, while also shipping citation-grounded RAG features that drove user trust and adoption.”

“BlackRock application engineer/product owner focused on enterprise AI platforms, building internal GenAI and ML workflow products for operations and business teams. Stands out for combining consultative solution design with hands-on implementation, including a contract review platform that cut first-draft review time by 60%+ and an AI mailbox tool that drew interest from 17 additional teams during the POC stage.”

Harsh Sanas

Screened

Intern Full-Stack Engineer specializing in AI and distributed systems

Los Angeles, CA2y exp

“Full-stack product engineer who has designed and shipped production web experiences in EV charging, trading, automotive companion apps, and AI systems. Stands out for owning user-facing React experiences through backend integration and production monitoring, with a strong bias toward reliability in real-time and high-stakes workflows. Also has early-stage Scale AI experience building a Text-to-SQL agent stack with Python, PostgreSQL, Redis, Kafka, and AWS.”

Sarthak Gupta

Screened

Mid-level AI/ML Engineer specializing in LLMs, NLP, and real-time AI systems

New York, NY4y exp

“Backend engineer who built a real-time pipeline for recording, transcribing, and analyzing audio from 400+ news radio stations, scaling Whisper on an HPC cluster with 7 H100 GPUs. Has strong performance optimization experience (30% latency reduction via SQL/query design; 50% DB call reduction via Redis caching) and has implemented region-based data isolation and PII protections in a regulated environment (JP Morgan Chase).”

“Analytics professional with experience at McKinsey & Company and Dell Technologies, focused on turning messy operational and business data into trusted dashboards and decision tools. They combine SQL, Power BI, and Python to solve data quality issues, define metrics like retention, and deliver measurable impact such as a roughly 30% reduction in manual reporting time.”

Hansraj Pabbati

Screened

Senior Software Engineer specializing in AI/LLM systems and cloud backend platforms

Remote, CA8y exp

“Built and owned an end-to-end AI-powered natural-language-to-SQL deployment within Oracle OCI/Autonomous Database, including enrichment pipelines, RAG-based retrieval, SQL generation APIs, and post-launch monitoring. Stands out for combining LLM production engineering with strong guardrails, stakeholder management, and operational rigor around accuracy, latency, hallucination mitigation, and reliability.”

“Healthcare-focused full-stack/platform engineer with recent hands-on experience in Go, React, Python, Kubernetes, and AWS. They have worked in high-reliability, compliance-heavy environments, driving infrastructure modernization, internal operational tooling, and observability improvements that reduced troubleshooting friction for clinician platform and support teams.”