Vetted Supabase Professionals

Karan Savaliya

Screened

Junior Backend Engineer specializing in data platforms and cloud APIs

Remote, USA1y exp

“Backend lead at a stealth startup and builder of MailIQ/MailBox—an automated Gmail inbox digest + cleanup system. Designed secure multi-account email ingestion and cost-efficient LLM-based summarization, and implemented robust unsubscribe automation using Playwright + OpenAI webpage analysis (including captcha-handling) with strong safety guardrails, incremental rollouts, and rollback strategies.”

Leon Hsi

Screened

Mid-level Solutions Consultant / Full-Stack Developer specializing in APIs, SQL, and cloud systems

Houston, TX4y exp

“Builder with hands-on security hygiene experience from developing a helpdesk portal handling sensitive payment/invoice data, focusing on RBAC, least-privilege integrations (QuickBooks/Atera), and tightening API authorization to prevent cross-account access. Also built personal projects integrating Twilio/Callkeep/Supabase/OpenAI with strong key management and defensive handling of real-world API/network failure modes; holds an ISC2 certification and is actively deepening cloud security skills.”

Monish Reddy Bonthu

Screened

Mid-Level Full-Stack Engineer specializing in real-time systems and FinTech

FL, USA5y exp

“Backend engineer with hands-on experience modernizing a real-time logistics/tracking platform from a tightly coupled polling architecture to a service-oriented/microservices design using Node.js and WebSockets. Emphasizes contract-first FastAPI development, defense-in-depth security (JWT/OAuth, RLS/Supabase), and safe incremental migrations with feature flags and strong observability, delivering sub-second updates and improved performance under peak load.”

“Independent builder of production-grade systems: shipped an end-to-end URL shortener with JWT auth, Redis rate limiting/caching, Postgres, Docker, and real-time analytics, and separately architected a Redis-backed distributed task queue handling 1000+ tasks/min. Demonstrates strong distributed-systems instincts (atomicity, retries/DLQ, idempotency, heartbeats) plus a focus on maintainable code and self-documenting APIs (FastAPI/OpenAPI, versioned routes).”

“Backend engineer who built a production retrieval-augmented narrative analysis platform for 100-page screenplays using a Node/Express orchestrator and a Python/FastAPI AI engine, including a key redesign from disk-based uploads to in-memory streaming to eliminate Windows file-lock failures. Also led a refactor of a municipal vehicle tracking system into a C-based distributed engine handling 4M+ daily packets with 99.99% data integrity and automation that reduced manual ops by 50%.”

Abhinav Malkoochi

Screened

Junior Full-Stack Engineer specializing in AI and automation

Dallas, TX1y exp

“Startup-focused builder who created and iterated an MVP for Enky, a two-sided marketplace connecting music artists and creators, informed by hundreds of customer interviews. Implemented CI/CD, monitoring (PostHog/Sentry), and a complex payout pipeline involving scraping social platforms and routing escrow payments via Stripe, and has a track record of quickly debugging production issues (e.g., iOS-specific OAuth cookie failures).”

Ofek Shaltiel

Screened

Mid-Level Full-Stack Software Engineer specializing in AI-enabled web platforms

Dallas, TX4y exp

“Backend/AI engineer in construction tech (HyperWater AI) who delivered major production performance wins (analytics API from ~1 hour to 0.5s) and shipped LLM features for parsing subcontractor manifests into CSI divisions with human-in-the-loop review. Also built a freelance agentic document-verification system using OCR + RAG over pgvector with robust retry/escalation logic and user feedback loops.”

“Software engineer with a pragmatic, production-oriented approach to AI-driven development, using AI to accelerate coding while keeping human oversight on correctness, architecture, and final decisions. Has hands-on experience with agent-style AI workflows and has led the design and coordination of AI-agent systems with a strong emphasis on reliability, performance, and end-to-end execution.”

“Full-stack developer who built a desktop business application combining invoicing, bookkeeping, user management, and an AI assistant layer. They work across React, Node.js, Supabase, Redis, and deployment automation, and emphasize reusable component architecture that they say cut project delivery time from 2-3 months down to as little as 2 weeks.”

“Backend engineer who owned the API layer for an AI trust/analytics dashboard (trust scores, stability checks, public verification endpoints) using Python/FastAPI and Postgres. Has hands-on DevOps experience deploying FastAPI and Node.js services to AWS Kubernetes with GitHub Actions + ArgoCD GitOps, plus Kafka-based real-time event streaming and careful staged migration practices (shadow traffic/dual writes, rollback planning).”

“Product-minded engineer who has owned features end-to-end, including a full onboarding redesign that lifted completion ~25% and a production LLM/RAG report-generation system with strong guardrails (schema-constrained JSON, confidence gating, logging) and an automated eval/regression loop built from real user queries. Also built a scalable research data pipeline ingesting messy PDFs/JSON/CSVs with normalization, idempotent reruns, observability, and cost/latency tradeoffs.”

“Software engineer who built an AI marketing/outreach agent end-to-end: Next.js (App Router + TypeScript) frontend integrated with a Python/Django REST backend using LLMs (Gemini, ChatGPT-4o) and SQL databases. Demonstrated measurable performance wins—improved a 100k-record UI by 15% (Lighthouse) and cut a Postgres-backed search API from ~3s to ~1ms via indexing—while also owning post-launch monitoring (webhooks/cron, New Relic/CloudWatch) and customer support.”

Taruni Reddy Ampojwala

Screened

Mid-level GenAI Engineer specializing in LLM agents and RAG systems

Brooklyn, NY4y exp

“Built and deployed a production RAG-based LLM assistant that answers day-to-day operational questions from internal PDFs/SOPs, with strong emphasis on data consistency (metadata versioning, confidence thresholds, conflict handling) and low-latency retrieval at scale. Experienced designing and orchestrating multi-agent LLM workflows (retrieval/validation/generation) and pipeline orchestration for ingestion/embedding/vector-store updates, plus iterative delivery with non-technical operations/business stakeholders.”

Yashwant Gandham

Screened

Junior Machine Learning & Backend Engineer specializing in LLM systems and ML infrastructure

Boulder, CO1y exp

“Built and deployed production RAG-based document search/Q&A systems (DocChat and an internship marketing RAG), using a React + FastAPI stack on GCP with docs stored in GCP buckets and retrieval via embeddings/vector DB. Emphasizes cost/performance tradeoffs (reported ~40% cost reduction) and ships via Docker (Railway), with load/API testing using JMeter and Swagger; regularly collaborates with a CEO stakeholder to iterate and push changes to production.”

“Software/product engineer who has owned a consumer iOS dating app from customer discovery through roadmap execution, while also shipping an in-app LLM-powered support/feedback bot. Brings a mix of product sense and backend systems experience, including rebuilding a race-condition-prone event orchestration system and designing microservices to handle arbitrary black-box production data.”

Adam Hinton

Screened

Mid-level Full-Stack Engineer specializing in Healthcare IT and SaaS

Philadelphia, PA3y exp

“Backend/data engineer with healthcare/medical-device experience who scaled a HIPAA-constrained local data platform to 50k users and 1B+ records, boosting processing from <500 to ~50k datapoints/sec using sharding, indexing, and SQL over Parquet (DuckDB). Strong security and reliability focus (JWT/RLS, least privilege, heavy unit/E2E testing) and a TypeScript specialist who uses type design to eliminate edge-case failures.”

Jookwang Jung

Screened

Staff Software Engineer specializing in AI-native platforms and full-stack systems

Los Angeles, CA20y exp

“Founding engineering lead and player-coach who built a small cross-functional team around an AI-first development model, increasing execution speed by more than 3x through hands-on coaching and human-in-the-loop practices. Has led architecture for a multi-tenant platform and built an AI system for fashion businesses that generates content, imagery, and operational recommendations, combining technical depth with product and UX judgment.”

Nicholas Gallo

Screened

Senior Game Engineer specializing in Unity, Unreal, and multiplayer systems

Remote10y exp

“Unity/C# gameplay engineer with strong systems and optimization focus, including architecting a networked ability system that reduced networking load via lightweight ID-based replication. Also brings experience with AI-driven game tooling for Meta, custom internal tools, and mobile multiplayer performance tuning across graphics, networking, and input architecture.”

“Built and deployed a "personal second brain" product (CloneMind) with an end-to-end RAG pipeline for retrieving information across PDFs, URLs, images, and audio using Next.js/Node.js/Postgres/Supabase/Redis. Demonstrates strong practical depth in retrieval quality tuning, latency reduction via caching, and stateful orchestration with LangChain/LangGraph, plus experience persuading a non-technical professor stakeholder by shipping a working prototype.”

“Built and shipped a production LLM-powered incident response agent for a microservices platform, automating alert triage and safe remediation recommendations with strong guardrails (RAG grounding, structured JSON outputs, rule-based validation, and human-in-the-loop). Implemented state-machine orchestration (Redis/Kafka), comprehensive eval/monitoring, and an error categorization pipeline that cut hallucination errors ~40% and reduced MTTR ~30%.”

Vishnu Priyan Sellam Shanmugavel

Screened

Mid-Level Applied AI Engineer specializing in LLM services, RAG, and OCR/NLP extraction

Arlington, VA4y exp

“Backend/platform engineer who built and evolved a large-scale healthcare document processing system (OCR + LLM orchestration) in Python/FastAPI on Google Cloud (Cloud Run, GCS, Firestore), processing ~1.5M files per batch and tens of millions overall. Emphasizes reliability and operational safety via deterministic IDs, idempotent state machines, strong observability, and self-healing reconciliation, plus disciplined migrations using dual-run validation and incremental rollouts.”

Sahilsingh Harcharansingh Khalsa

Screened

Junior Full-Stack/Product Engineer specializing in Next.js, TypeScript, and AWS backends

Seattle, WA2y exp

“Full-stack engineer with startup-style end-to-end ownership, recently shipping a production dashboard at Find Me LLC using Next.js App Router/TypeScript with Supabase + Azure Blob Storage for secure asset/document uploads. Strong server-first React performance mindset and hands-on Postgres modeling/query optimization (EXPLAIN ANALYZE), plus experience building resilient AWS event-driven workflows with idempotency, retries, and DLQs.”

Pathapati aditya

Screened

Mid-level Design Engineer specializing in product design and front-end implementation

New York City, NY4y exp

“Product designer-builder who codes, with a rare end-to-end profile spanning product strategy, UX, design systems, frontend engineering, and AI integration. Built PetCloud/PetClub as a fully solo product in Next.js/TypeScript/Supabase and has also driven measurable UX gains in company settings, including 25% higher engagement, faster design-to-dev handoff, and fewer UI bugs.”

“Full-stack developer with seven years of experience who has built production systems across AWS serverless infrastructure, Django applications, and user-facing web products in domains like emergency response, fintech/investing, recruiting, and conference management. Particularly notable for combining technical architecture with product thinking—e.g., modernizing a crash-tracking platform for emergency responders and materially improving trust-driven conversion in a trading-card fractionalization product.”