Vetted Supabase Professionals

“AI Engineering Intern at The Etherloop building the backend for a healthcare lifestyle recommendation app, including a multi-agent RAG-based system that uses curated SME data plus web search to generate personalized supplement recommendations from user lifestyle details and blood biomarkers. Evaluates against 500+ SME ground-truth profiles with ranking metrics and focuses on HIPAA-aligned deployment, privacy/security, and guardrails to reduce hallucinations and unsafe outputs.”

“Full-stack product engineer who independently built and maintained a marketplace app end-to-end, including React/Node/PostgreSQL architecture, real-time chat, S3 uploads, and OpenAI-based listing validation. Also shipped backend-heavy workflow features for a nonprofit upward mobility platform using Supabase edge functions, with a clear focus on security, role checks, and operational reliability.”

“ML/AI engineer with hands-on ownership of production recommendation and GenAI systems, spanning experimentation, deployment, monitoring, and iteration. Stands out for delivering measurable outcomes—22% CTR lift, 15% conversion lift, and a 30% reduction in support tickets—while demonstrating strong judgment on latency, cost, and safety tradeoffs in real-world systems.”

“Software engineer with 2 years of professional full-stack experience plus a CS master's journey in the US, who has since focused heavily on building hackathon-winning AI systems. Stands out for combining production-minded backend architecture, TypeScript-heavy reliability work, and multi-agent LLM applications spanning physical security and insurance claims automation.”

“Built a production AI-powered sales automation system that reads inbound product enquiry emails, extracts structured data, and routes decisions via a rules-based workflow integrated with a product database. Leverages Gemini structured outputs/schema plus option-based prompting and validation to keep responses reliable, and optimizes latency by breaking agent reasoning into smaller LLM calls; evaluates workflows with LangSmith and metrics like completion rate and accuracy.”

Tarang Nair

Screened

Mid-Level Full-Stack Software Engineer specializing in cloud-native APIs and AI apps

Oakland, CA5y exp

“Early-stage startup full-stack engineer who built an inventory and billing management product for retail shop owners in India, iterating quickly based on customer feedback. Has hands-on experience designing scalable cloud infrastructure on GCP with Kubernetes and applying clean architecture for testable, modular systems; also built internal monitoring dashboards for error and performance visibility.”

“Backend Python engineer focused on production-grade automation and reliability, with hands-on experience designing scalable API systems on PostgreSQL and making pragmatic architecture calls (modular monolith over premature microservices). Demonstrated measurable performance wins (50–60% latency reduction) and strong operational rigor via observability, incremental rollouts/feature flags, and security patterns like JWT + RBAC + database row-level security.”

“Backend engineer with insurance/claims domain experience who modernized legacy claims processing systems to support AI-assisted claim review. Emphasizes production-ready API design in Python/FastAPI (schemas, async, caching, graceful degradation), strong observability with Prometheus, and layered security including JWT auth plus database row-level security (Supabase/Postgres).”

“Backend engineer with experience in both healthcare (Siemens) and payments (Bitwise), focused on scaling Python APIs and modernizing architectures. Has led monolith-to-microservices migrations and introduced Kafka async processing, Redis caching, and ELK observability, citing ~40% faster issue resolution and improved reliability via idempotency and strong security controls (OAuth2/JWT, RBAC, RLS).”

“Front-end-focused developer at a startup who also handles back-end integration, delivering customer-facing analytics dashboards from Figma designs while collaborating tightly with UX/product and running customer review cycles. Recently helped manage a risky production user-migration issue by stopping deployment, restoring deleted records from backups, and rebuilding the migration process with a safer test environment and validation.”

Abhishek Gupta

Screened

Mid-level Full-Stack Developer specializing in AI automation and RAG pipelines

Toronto, ON6y exp

“Frontend engineer who has led mobile-first and web React/TypeScript products end-to-end, including an expense tracking app handling sensitive financial data and a real-time messaging/activity dashboard with chat, presence, and contextual side panels. Emphasizes scalable architecture, rigorous component-boundary testing, and production-safe rollout practices (feature flags, analytics/logging, staged releases) to ship reliably in fast-paced environments.”

Nicholas Kim

Screened

Entry-Level Software Engineer specializing in full-stack and backend development

Houston, TX0y exp

“Full-stack developer who built a workout tracker feature end-to-end on the PERN stack (Postgres/Express/React), including relational schema design, REST APIs, and optimistic UI updates. Also has Next.js App Router experience (dynamic routes, SSG/React Server Components) and a strong quality mindset from Boston Scientific, where they used TDD to support clinically sound ECG analysis software and drove backend test coverage to 97%.”

“Full-stack AI engineer who has owned production deployments for both a voice journaling/emotional insights app and a RAG-based research assistant. Stands out for turning messy, failure-prone LLM and document pipelines into reliable user-facing systems through strong debugging, staged workflow design, and post-launch stabilization.”

Kevin Delong

Screened

Senior AI/ML Engineer specializing in Generative AI, LLMs, and RAG systems

Irvine, CA12y exp

“AI/ML engineer with hands-on experience shipping production systems across fintech, travel, and legal use cases. They’ve built end-to-end chatbot, generative content, and RAG solutions on AWS with CI/CD, monitoring, and guardrails, including a loan application platform that generated $3,000 in sales in its first month.”

“Full-stack developer who has built and shipped both traditional web products and AI-powered applications, including a Spotify playlist-combining app, a recruiter-facing RAG chatbot embedded in a portfolio, and a fine-tuned GPT-2 art review generator. Stands out for combining privacy-conscious engineering, practical LLM guardrails, and scrappy production problem-solving—from queue-based inference systems to newsroom digital transformation with multi-tenant WordPress.”