Vetted TypeScript Professionals

“Backend/ML systems engineer with experience at SDSC, UCSD, and Media.net, building production semantic dataset/model discovery using embeddings + Solr KNN and LLM-based intent/reranking at 5M+ dataset scale. Emphasizes offline/online separation for predictable serving, has delivered measurable gains (23% retrieval accuracy, 38% latency reduction) and helped secure a $3M+ NSF grant.”

“Data Analyst at U.S. Cellular who built production LLM solutions, including a Tableau-embedded chatbot that converts natural language questions into Oracle SQL and returns actionable KPI insights for non-technical users. Also authored MAD-CTI, a multi-agent LLM system for dark web hacker forum threat intelligence (published in IEEE Access) that outperformed single-agent approaches by 14%.”

“Engineering leader with BCG consulting background who has built roadmaps and scaled AI and data platforms for pharma and manufacturing clients. Led architecture shifts (Django monolith to event-driven microservices) for high-volume IoT SaaS products, improving deployment speed and enabling zero-downtime releases. Also established a near-shore engineering team in São Paulo and has managed distributed teams across multiple countries, leveraging strong stakeholder communication and a prior professional acting background for storytelling.”

“Frontend-focused JavaScript engineer who built Collabsync with real-time chat and file sharing using Socket.io, emphasizing reusable components, clear event contracts, and performance (minimizing React re-renders). Has experience at PwC building internal React/Angular dashboards and documenting insurance APIs, plus a contract role at Capital One delivering in fast-changing, loosely defined environments.”

“Open-source contributor to a JavaScript visualization library who focused on runtime/rendering performance—eliminating unnecessary full redraws via memoization and diff-based updates validated with Chrome profiling. Also strengthened the project’s developer experience by adding TypeScript definitions, writing practical documentation, building minimal example apps, and handling community issues with reproducible debugging and public fixes.”

“20-year technology builder with ~8 years in healthcare AI, currently at Keycentrix modernizing a legacy pharmacy solutions business. Shipped an OCR MVP within days and delivered a rebate-based product generating ~$50K/month, leveraging Claude/LangGraph agentic automation to replace work typically requiring a much larger engineering team. Developing a "Longevity AI Copilot" B2B platform that synthesizes research, labs, and wearable data into personalized longevity protocols for HNW and corporate wellness markets; concept validated but not yet incorporated or funded.”

“Interned at Paycom and shipped a productionized ML/AI system that automatically regenerates XPath selectors to self-heal Selenium UI tests when the DOM changes. The pipeline handled 1,000+ failing tests/hour with ~90–95% auto-fix accuracy, using confidence thresholds, human-in-the-loop fallbacks, logging/dashboards, and retraining loops to manage distribution shift and maintain reliability.”

“Unity/C# game developer who led a major level-system overhaul at Codable to support seed-based procedural generation, more than doubling available levels and improving engagement and paid conversion. Also shipped an OpenAI-powered in-product assistant (CatBot) for a kid-focused creator tool, including prompt screening, safety/tone constraints, and cost controls via daily usage limits.”

CHIRAG ARORA

Screened

Senior Backend Software Engineer specializing in microservices and cloud platforms

San Jose, CA9y exp

“Backend engineer with PayPal experience building a high-reliability onboarding API platform (Java/Spring Boot) integrating KYC/compliance and serving 1M+ users annually. Also shipped an internal LLM-driven developer tool that automates PR review insights and OpenAPI documentation with rigorous evaluation, schema-bound guardrails, and production observability.”

“Backend/AI engineer who has built an offline, citation-grounded RAG system end-to-end with hybrid retrieval, local LLM inference, and quantitative evaluation via RAGAS. Also brings real-time systems experience from an Airbnb-like booking platform and data pipeline/ML quality work from a Bilibili internship, with a strong emphasis on reliability, privacy, and measurable correctness.”