Vetted Vercel Professionals

“Built and iterated an end-to-end virtual waiting room for a real-time ticketing prototype, making concrete architecture tradeoffs (polling + Redis Pub/Sub) and improving performance post-launch with Redis caching (+30% throughput, -15% p99 latency). Also has hands-on experience building Spark/HDFS ETL pipelines with strong reliability/observability patterns and running disciplined NLP model evaluation loops on review-rating classification.”

“Built Taskline end-to-end as a solo founder: an AI-native operations platform for tradespeople with web and mobile apps, AI receptionists, invoicing, scheduling, and payments. Particularly interesting for teams seeking a zero-to-one full-stack builder who can turn LLM/agent capabilities into practical, low-friction products for non-technical users.”

“Full-stack engineer who built an internal user-activity tracking and reporting system end-to-end using React/TypeScript, Node/Express, and Postgres, deployed on AWS (EC2/ALB, S3/CloudFront) with CloudWatch observability. Emphasizes reliability and data correctness via idempotent ingestion, retries with exponential backoff, backfills/reconciliation, and performance tuning as data scales, and has experience shipping quickly in ambiguous early-stage startup conditions.”

“Built a personal product, Pilly AI—an AI-powered e-commerce product Q&A widget embedded via a simple script tag and served via Cloudflare CDN—covering landing page, backend, database, and deployment end-to-end. Implemented OpenAI integration with prompt/context engineering, JWT-authenticated APIs, and Postgres (NeonDB), and successfully sold the product to a client while shipping in roughly two weeks.”

“AI/LLM engineer who built a production autonomous GenAI content ecosystem that generates short-form scripts, extracts viral highlights from long-form video, and dubs content into 33+ languages. Focused on making LLM outputs production-safe via schema enforcement, token-to-time alignment, critic-agent verification, and scalable async orchestration—cutting manual workflows by ~90% and saving $200k+ annually.”

“Open-source contributor who improved React Query’s caching/subscription behavior to reduce unnecessary re-renders via debouncing and batched updates, validated with benchmarking and extensive tests. Also maintained a Flask extension and resolved production background-task hangs by tracing Redis connection handling issues, adding cleanup/retry logic and troubleshooting docs. In a fast-paced startup, owned the design of a Celery+Redis multi-queue background processing system with Prometheus-based observability.”

Antonio Pavicevac-Ortiz

Screened

Senior Full-Stack Engineer specializing in React/Next.js for FinTech and media

11y exp

“Built Hillfinder, a self-directed terrain-aware navigation app for cyclists, runners, and skaters, using React, Next.js, TypeScript, MongoDB, and Mapbox. Stands out for owning a complex browser-based geospatial UI and solving tricky async state and loader synchronization issues with event-driven architecture while emphasizing polished, trustworthy UX.”

“Frontend-focused engineer with experience spanning AI interview coaching and institutional financial dashboards. Stands out for combining user-centered UI design with deep browser-performance work, including reducing dashboard lag via virtualization and building low-stress, progressive-disclosure interfaces for anxious job seekers.”

“Open-source JavaScript contributor focused on performance and maintainability in data visualization libraries—refactored legacy ES5 into modular ES6, added tests/docs, and delivered ~30% faster load times with positive community adoption. Also optimized a React dashboard (~40% load-time reduction) and took ownership in an ambiguous AI product initiative by setting milestones, standing up an initial ML pipeline, and shipping a prototype in ~6 weeks that became the basis for production.”

“Frontend engineer focused on marketing and analytics products, including a real-time multi-touch attribution dashboard. Uses Next.js (SSR/ISR) with React/TypeScript and Tailwind, and emphasizes quality at scale via automated testing, CI/CD (GitHub Actions), feature-flagged staged rollouts, and Mixpanel-driven iteration. Experienced modern state management patterns (React Query + Zustand) and performance tuning (code-splitting, dynamic imports, lazy loading).”

“Solo-built and launched an AI logo generator SaaS in ~2 months using React/Next.js/TypeScript with managed auth and payments, deploying via Vercel/GitHub CI/CD. Also has hands-on AWS production experience running containerized services with Terraform-managed multi-environment infrastructure and strong reliability patterns for integrations/pipelines.”

“Engineering Manager/player-coach leading Data Infrastructure, ML/DS, and AI Engineering pods who recently shipped multiple production agentic GenAI features. Built privacy-preserving LLM workflows (PII redaction via Microsoft Presidio) and drove an AI expense-approval agent from ambiguous ask to GA, cutting approval time from ~2.5 days to <4 hours with >85% accuracy. Also owned a major LLM cost overrun incident and implemented cost observability plus circuit breakers to prevent runaway agent loops.”

“Built and owns the full ProteinMenus AI pipeline end-to-end, spanning the iOS client, FastAPI backend, Gemini integration, Firestore, and Cloud Run deployment. Strongest signal is full-stack product ownership in an AI-driven consumer workflow, including monetization logic via an atomic credit system and architecture choices optimized for fast iteration after launch.”

Aravind Mohan

Screened

Junior Software Engineer specializing in AI agents and backend systems

Seattle, WA5y exp

“Backend/AI workflow engineer who built a production event-personalization service (FastAPI + AWS Lambda) and solved real-world reliability/latency issues with deterministic routing, caching, and query/index optimization. Also built an end-to-end Gmail-based job application tracking agent using a lightweight RAG pipeline with Gemini, strong guardrails (Pydantic schemas, confidence thresholds), and offline regression tests to prevent drift and hallucination-driven data corruption.”

“Full-stack engineer with 6+ years of experience building high-scale internal products and AI-powered workflows, including a U.S. Bank payment operations dashboard handling 500k+ transactions and real-time analyst collaboration. Stands out for true end-to-end ownership—from React/TypeScript frontend architecture to Node/Spring services, PostgreSQL/Redis optimization, Kubernetes deployment, and Datadog monitoring—plus measurable impact on adoption, latency, and analyst efficiency.”

Supreet Purthpli

Screened

Mid-level AI/ML Software Engineer specializing in cloud-native MLOps and FinTech

San Francisco, CA4y exp

“Software engineer with JPMorgan Chase experience delivering end-to-end fintech features (Next.js/React/Node/Postgres on AWS) and measurable performance gains. Built and productionized an AI-native credit decisioning workflow combining LLMs, vector retrieval, and a rules engine with strong governance (bias checks, auditability, human-in-loop), improving precision and cutting underwriting turnaround time by 40%.”

Ajay Desai

Screened

Mid-level Full-Stack Software Engineer specializing in FinTech and backend platforms

USA4y exp

“Built an AI-native legal research platform that automated analysis across 100,000+ dense legal documents, combining LLM workflows, async backend architecture, and conversational retrieval in production. Also brings cross-domain experience in investment-analysis agents and healthcare claims/billing systems, with a strong emphasis on reliability, deterministic orchestration, and safe handling of messy operational data.”

Sanket Mungikar

Screened

Mid-level Software Engineer specializing in distributed backend and AI analytics platforms

California, USA4y exp

“Full-stack engineer at BigCommerce who combines customer-facing deployment ownership with hands-on AI/LLM systems work. Built and launched merchant analytics and predictive inventory workflows using React, TypeScript, FastAPI, Kafka, AWS, and RAG-style architectures, and has real production experience debugging non-deterministic AI issues caused by data pipeline freshness and event-ordering problems.”

“AI/full-stack engineer with experience spanning startup product building at Twinly, enterprise analytics at Zoho, and high-stakes life sciences ML at Wave Life Sciences. Stands out for combining React/TypeScript + FastAPI product execution with rigorous AI evaluation, retrieval optimization, and human-in-the-loop design, delivering measurable outcomes like 75% fewer analytics requests, 20% fewer failed experiments, and MVP delivery 3 weeks early.”

“Built and deployed LLM-powered agentic systems including a multi-agent travel planning assistant using LangChain, RAG (FAISS), real-time APIs, and a supervisor agent to manage coordination and reduce hallucinations. Also developed a Text-to-SQL system with schema-aware validation guardrails, and collaborated with drilling domain experts at CNPC USA to build an ML model predicting rate of penetration (ROP).”

Nithyashree Raghunathan

Screened

Mid-level Full-Stack AI Engineer specializing in agentic systems

Santa Clara, CA5y exp

“QA/data pipeline engineer with hands-on AI product building experience, spanning enterprise AWS migration testing for Belgium postal services and personal multi-agent systems in fintech and recruiting. Stands out for combining rigorous validation and production stability work with modern LLM orchestration, guardrails, and messy-document normalization workflows.”

“Full-stack product engineer who has built both operational systems and enterprise AI copilots in production. They owned an AI-powered inventory platform end-to-end, driving a 45% drop in stock issues, and also shipped a Microsoft Teams-based HR/IT copilot using RAG and workflow automation that reduced repetitive support queries by roughly 30%.”

“Product leader with experience rebuilding teams and platforms in both healthcare and AI-enabled SaaS environments. Most notably rebuilt Uniform Teeth’s product organization, EMR, and patient app after a restructuring, contributing to major clinical efficiency gains and the company’s acquisition by Impress in 2022. Brings a strong human-in-the-loop AI philosophy, plus experience leading PMs, design, and engineering through high-change environments.”

“ML practitioner with hands-on experience in healthcare time-series modeling (CGM-based blood glucose prediction) including a novel ICA-based blind source separation approach and robust data-cleaning for noisy, missing sensor data. Also built an embeddings + LLM-powered podcast recommendation workflow using YouTube transcript scraping and Vellum AI document indexing, with a strong emphasis on production-grade engineering practices (TDD, monitoring) and realistic rolling validation for forecasting.”