Vetted Supabase Professionals

“Backend/data engineering candidate with Netflix experience designing and migrating analytics platforms from batch to real-time streaming (Kafka/Flink) across AWS and GCP. Delivered measurable improvements (40% lower data delay, 99.9% accuracy) using phased rollouts, automated data validation (Great Expectations), and strong observability (Prometheus/Grafana), and proactively hardened pipelines with idempotency to prevent duplicate Kafka processing.”

Andrew Liang

Screened

Intern Software Engineer specializing in full-stack and AI/ML systems

2y exp

“Software engineer with experience at Amazon and Agora building end-to-end systems: a knowledge-base AI chatbot (React/TypeScript UI + retrieval/response backend + Docker deployment) and an internal approval governance platform using AWS Step Functions and DynamoDB. Emphasizes fast iteration without sacrificing trust via feature-flag rollouts, citation-required answers, abstention on low-confidence retrieval, regression query sets, and strong observability (request IDs, structured logs, latency/error monitoring).”

Kshitij Kakade

Screened

Intern-level AI/Full-Stack Engineer specializing in LLM systems and RAG

Mountain View, CA2y exp

“Product intern at KNEX who stepped into engineering ownership when the team was reassigned and shipped a lease abstraction AI agent that cut data-entry time by roughly 85% in a live CEO demo. Also built a full-stack agentic RAG app with React/TypeScript, FastAPI, pgvector, hybrid retrieval, tool use, and citation-based transparency, showing unusually strong end-to-end AI product depth for an early-career candidate.”

“Internship experience deploying cloud-based services into production, including navigating security/resource provisioning and coordinating approvals across impacted teams. Built a Python backend for a local Ollama-based app using open-source models, and has hands-on distributed systems experience implementing and debugging Paxos with extensive logging/state tracing.”

“Engineering lead/player-coach at Sam's Club who drove major React Native Member Desk Returns changes (including Vision Center insurance refund flows) by coordinating Product/UX, QA, and API teams and leading a 3-engineer squad. Also led an e-commerce mobile OAuth migration, evolving a login experience from vanilla JS to a React SPA as security requirements (MFA, reCAPTCHA) emerged, and improved production reliability via better request flagging and analytics/log instrumentation.”

“Full-stack product engineer who built a semantic word game end-to-end across web and mobile, including a custom ML-based scoring pipeline that replaced an expensive third-party API. Also has experience shipping real-time social learning features at BU Spark, with strong instincts around product ownership, UX polish, and pragmatic infrastructure choices.”

“Backend-leaning engineer with experience at Dropbox, Wayfair, and Etsy who has led cross-product integrations and internal platform tooling. Re-architected a legacy promo code system from a PHP monolith to a Java/Spring Boot microservice achieving a 99% execution-time reduction, and built a React/TypeScript + Supabase product (press.social) with LLM-powered bulk parallel generation and a path to multi-tenancy.”

Daniel Luzzatto

Screened

Junior Machine Learning Engineer specializing in LLMs, computer vision, and robotics

Tirat Carmel, Israel1y exp

“Built and deployed an agentic, multimodal LLM system that automates privacy redaction pipelines (audio/video/tabular) using LangChain orchestration and a closed-loop self-correction design. Personally implemented and performance-optimized core CV tooling (face blurring with tracking/Kalman filter) achieving >100 FPS on CPU, and validated reliability with golden-dataset benchmarking across 100+ privacy intents and measurable redaction metrics.”

Yash Jajoo

Screened

Senior Software Engineer specializing in AI and FinTech platforms

New York City, NY8y exp

“Built a production LLM pipeline at Walter AI that scans massive user inboxes, identifies financial newsletters, and extracts trading strategies into structured JSON for downstream paper-trading workflows. Stands out for combining agent architecture with strong production discipline—cutting scan time from 20 to 5 minutes, reducing LLM costs by 90%, and achieving 3-second P99 latency while handling messy, inconsistent email data at scale.”

“Built internal banking operations products spanning full-stack dashboards and AI-assisted workflow tools. Shipped a RAG-based assistant using PostgreSQL/Redis retrieval with OpenAI and Claude APIs, and used audit-log measurement plus evals/traces to drive a reported ~30% reduction in manual review time.”