Vetted TypeScript Professionals

Jyothsna V

Screened

Mid-level Backend Software Engineer specializing in Python/FastAPI and cloud-native microservices

USA4y exp

“Backend engineer who evolved Coca-Cola bottlers' Trade Promotion Optimization platform at Coke One North America, building domain-focused microservices in Node.js and Python (Flask/FastAPI) with PostgreSQL. Experienced in multi-tenant security (OAuth2/JWT, RBAC, row-level scoping by bottler/region), API contract/versioning discipline, and Azure DevOps-driven incremental rollouts with strong observability.”

JUNG NO

Screened

Senior Solutions Engineer & Applied AI Builder specializing in agentic workflows

Remote, US9y exp

“Built and shipped a production AI booking/quoting system for a Spanish-speaking cleaning business serving English-speaking customers, covering the full booking and payment flow and generating bilingual SEO/AEO content. Uses Gemini/Genkit with multi-agent orchestration (ADK/MCP, LangChain) and a production stack on Vertex AI + Cloud Run + Terraform, with analytics wired from Google Analytics to BigQuery for measurable agent performance.”

Mrudula Devaguptapu

Screened

Mid-Level Software/AI Engineer specializing in backend systems, data pipelines, and RAG automation

United States3y exp

“Backend engineer with experience modernizing high-traffic subscription and payment systems (TCS) by moving to event-driven Spring Boot microservices with Kafka, adding idempotency/state management to eliminate duplicate processing. Built and scaled FastAPI services for AI automation workflows (360DMMC) with versioned contracts, JWT security, and strong observability, and has led live refactors using feature flags, parallel runs, and data reconciliation.”

Ali Zafar Zaidi

Screened

Senior Frontend/Full-Stack Developer specializing in React and Next.js

Hamilton, Canada8y exp

“Frontend engineer who has led architecture and delivery of large, data-driven React/TypeScript interfaces used by thousands of users, with strong emphasis on scalable structure, performance, and automated quality gates (CI/CD, Jest/Cypress). Recently building a resume screening application for McMaster University students and has experience shipping role-based (RBAC) features with tight cross-functional collaboration and PR-based QA deployments.”

Jasdeep Gill

Screened

Mid-Level Software Engineer specializing in AI and web development

Abbotsford, Canada5y exp

“Built an OCR backend that trains a custom Tesseract model for proprietary fonts and scales via multi-tenant isolation (tenant-scoped APIs, per-tenant storage, JWT+RBAC). Improved high-load image processing by shifting OCR to async worker queues and adding Redis caching, cutting processing time by ~66%, and also integrated Claude API to auto-generate test cases on code changes.”

chandankumar ramamurthy

Screened

Junior Full-Stack Engineer specializing in LLM-powered products

Washington, D.C.3y exp

“Built multiple systems from scratch at DSSD and Aglint, including an NGO sustainability reporting dashboard and a production LLM-powered phone screening agent using Twilio/Retell AI with RAG grounded in PostgreSQL candidate/job data. Strong focus on real-world reliability: guardrails, monitoring, and lightweight eval/regression loops that reduced recruiter score overrides by ~30%. Currently on OPT through May 2026 (plans STEM OPT extension) and committed to relocating to NYC for in-person work; seeking $90k–$120k base with meaningful equity for founding engineer roles.”

Shiva Dasi

Screened

Mid-level Software Engineer specializing in LLM agents and cloud-native systems

Boston, MA4y exp

“Built and shipped production LLM agents in compliance-sensitive environments (FERPA), emphasizing reliability via structured outputs, state-graph orchestration (LangGraph), and CI-driven eval/regression testing. Also has experience hardening messy ERP ingestion pipelines at scale (50K monthly orders) with normalization, idempotency/deduplication, and robust failure handling using AWS (SQS/CloudWatch) and PostgreSQL.”

“Candidate is targeting a CTO Venture Studio role and positions themself as a technical partner to founders rather than a founder personally. They demonstrate strong fluency in early-stage startup evaluation, especially around validating whether a product truly tests the business hypothesis and whether the underlying technology can scale significantly.”

“Full-stack product engineer focused on data-heavy dashboard applications, with hands-on ownership from React/TypeScript UI through Node/Express APIs to Postgres schema design and optimization. Stands out for combining product sense with engineering rigor: improving onboarding and reporting flows using analytics and user feedback, while also building reusable upload infrastructure and safe multi-tenant configurable experiences.”

“Full-stack product engineer with strong web3/blockchain experience, having built invoicing/tokenization, transaction explorer, and no-code workflow products across React, Node.js/serverless, and SQL. Stands out for combining deep technical execution with product thinking—improving onboarding completion by ~40%, shipping quickly under ambiguity, and creating reusable platform primitives for auditability and multi-tenant customization.”

“Built and deployed a full-stack web product for international football fans visiting the U.S. for FIFA, owning everything from crawling and aggregating event data to frontend, backend, deployment, and maintenance. Particularly strong in data-heavy product work, using LLMs, Google Maps API, and SQL/RPC patterns to improve data quality, speed implementation, and support a polished user experience.”

“Full-stack engineer who built a multi-tenant SaaS analytics dashboard end-to-end with Next.js App Router/TypeScript, emphasizing server components + React Query for performance and real-time UX. Demonstrated strong production ownership post-launch (observability, DB/query tuning, caching strategy) and has concrete wins like 30–40% load-time reduction and Postgres query latency cut to under 200ms.”

“Solo developer who built and operated an AI debate product end-to-end, from architecture and deployment through observability and post-launch stabilization. They show strong practical LLM production experience—using Vercel AI SDK, OpenAI, Langfuse, Mem0, and custom RAG—while improving latency to sub-4 seconds, driving failures near zero, and cutting LLM usage by 20%.”

Abhishek Ghaisas

Screened

Intern-level Data Scientist specializing in AI and full-stack applications

Pune, India1y exp

“Engineer with hands-on experience building production ML and Python backend systems, including a real-time social media monitoring pipeline handling 1000+ events per second and a prototype AI operations assistant for Seattle-Tacoma Airport. Stands out for combining reliability engineering, automation, and LLM/NLP-to-SQL work, with measurable impact such as improving uptime from 92% to 99.4%.”

“Healthcare software sales candidate with hands-on experience building outbound campaigns, refining ICP and messaging in a small, iterative environment, and selling AI-enabled EMR capabilities. Demonstrates strong consultative discovery in nuanced care settings like home health and home care, with measurable outbound results including a 12% reply rate, 5-10 demos, and 25% demo conversion.”

“Software candidate with hands-on experience using AI as a productivity multiplier across architecture, refactoring, testing, and code review. Has worked with LangChain-based multi-agent workflows to decompose complex engineering tasks for parallel execution, showing practical familiarity with emerging AI-native development patterns.”

“AI-focused software engineer who has built production CRM intelligence features including audio transcription, summarization, and action-item extraction, plus a multi-agent LLM/NLU pipeline using Supabase, Node.js, RabbitMQ, and CloudWatch. Stands out for a disciplined approach to AI-assisted coding: treating AI like a junior developer, rigorously testing outputs, and refining prompts to prevent hallucinations in real business workflows like resume screening.”

“Engineer with hands-on experience modernizing healthcare platform authorization and EVV compliance workflows, including replacing hardcoded permissions with a Cerbos-based RBAC/ABAC system. Stands out for pragmatic AI-assisted development in regulated environments, with a strong emphasis on testing, auditability, and catching subtle business-rule failures before production.”

“Builder of practical AI-backed products across developer tooling, travel search, defense, and healthcare-style workflows. They shipped an MCP/FastAPI/Gemma context-compaction system that cut token usage by about 80%, built a flight-price AI layer that validates LLM output against live search data, and helped shape a visionOS command center for a military air wing.”

Sarah Johnson

Screened

Senior Software Engineer specializing in web applications for aerospace and insurance

8y exp

“Frontend engineer with hands-on experience building complex React/TypeScript workflows in insurance and interactive Cesium-based geospatial applications. Stands out for combining UI implementation with business-rule enforcement, shared component library improvements, and pragmatic performance decisions across both frontend behavior and database design.”

“Built GRETA, a full-stack multi-agent AI platform for SEO content analysis and blog-writing support, combining React/TypeScript, serverless GCP Cloud Run workflows, and LLM/tool orchestration at scale. The system reportedly reduced manual analysis by 60%, and the candidate shows strong hands-on experience shipping AI products in ambiguous environments and refining them through internal user feedback.”

Shalini Jada

Screened

Mid-level Full-Stack Engineer specializing in Applied AI

Bridgeport, CT4y exp

“Full-stack AI engineer focused on operational and healthcare analytics use cases, with hands-on experience building React/TypeScript frontends and Node/FastAPI/Flask backends for agentic systems. Stands out for combining LLM orchestration, retrieval grounding, and human-in-the-loop controls with measurable business impact, including a fraud detection dashboard that achieved 92% accuracy and cut manual review time by 85%.”

“At ReferU.AI, designed and deployed an agentic RAG pipeline that automates multi-jurisdiction legal document drafting, emphasizing hallucination reduction through hybrid retrieval, validation agents, guardrails, and iterative regeneration. Experienced with orchestration frameworks (especially CrewAI) and rigorous testing/evaluation practices including human-in-the-loop review, adversarial testing, and production metrics/logging.”