Vetted React Professionals

“Full-stack engineer from early-stage startups who has owned AI products end to end, from B2B document intelligence platforms on AWS to an HVAC voice assistant and a GCP-based RAG research system. Stands out for combining hands-on backend/infra depth with team leadership in lean environments, and for shipping scalable AI systems that contributed to roughly 1 million yuan in sponsorship.”

“Front-end/UI engineer who built and standardized a complex metadata component system for 2,500+ banking users across 40+ internal product surfaces. Stands out for combining design-system architecture, browser-level CSS expertise, and workflow-sensitive UX for demanding financial users, including a token system that resolved 488 style references and a reusable responsive component library adopted across five product areas.”

Anson T P

Screened

Staff Software Engineer specializing in Salesforce, mobile, and IoT platforms

Bengaluru, India12y exp

“Frontend-leaning product engineer with strong end-to-end ownership across mobile observability, real-time systems, and scalable React/Next.js architecture. Built a reusable iOS logging framework integrated with Splunk, designed IoT operator dashboards, and shaped a JSON-driven website rendering platform by aligning product, backend, and frontend models.”

“Healthcare IT engineer with deep observability, backend, and database expertise who built mission-critical monitoring solutions across major enterprise transitions including Siemens, Cerner, and Oracle. Stood out by creating a DoD-deployed observability platform for 10,000+ HL7 interfaces and by automating hospital financial day-end workflows, cutting issues by 90% and freeing up 14 FTEs.”

“Software engineer intern at Nutanix who independently shipped and maintained an internal smoke-test/failure-analysis dashboard, integrating failure data from multiple upstream systems (e.g., Jira, Jenkins, CircleCI) via REST APIs. Also has prior data-science experience building Postgres-based asset management analytics with automated reporting and indexing for faster time-series retrieval.”

“Backend/data engineer focused on compliance and statistical processing systems on AWS, building containerized FastAPI services plus event-driven async workflows (Step Functions/EventBridge) with strong reliability patterns (JWT auth, idempotency, structured logging). Has modernized SAS-based batch pipelines into modular Python/AWS services with parallel-run parity validation, and has demonstrated measurable SQL performance wins (40+ min to <10 min) and hands-on incident ownership using CloudWatch-driven detection and prevention.”

“Undergraduate robotics researcher who built a crowd-aware motion planning system to navigate safely and efficiently through dynamic pedestrian environments, implementing the full pipeline in ROS (move_base, global planning, SLAM/localization) and validating via 2D crowd simulation. Also brings modern software delivery experience from web apps, including Docker/Kubernetes-based cloud deployment and CI/CD with automated testing.”

“Frontend engineer who led Take-Two’s first direct-to-consumer e-commerce platform, setting up a React/Next.js monorepo with CI/CD and semantic releases. Built a centralized, SSR-based payment micro-frontend (iframe + postMessage) integrating providers like PayPal/Braintree and Xsolla while meeting legal constraints around sensitive data, and drove measurable performance gains (~40% faster loads) through SSR and multi-layer caching (including Redis).”

“Java full-stack developer who has owned data-intensive, customer-facing and internal web products end-to-end (React/Angular + Spring Boot), including CI/CD and production support. Demonstrates deep microservices experience with RabbitMQ/event-driven architecture, idempotency, DLQs, and compensating logic to maintain reliability and data consistency at scale, plus a track record of replacing spreadsheet-based ops reporting with an adopted real-time internal tool.”

“Built and deployed a production LLM/RAG fraud investigation system to replace manual investigator workflows, combining transaction data, historical cases, and policy documents with agent-style steps and LoRA fine-tuning. Demonstrates strong reliability engineering (grounding, citations, abstention paths), performance optimization (retrieval/indexing/caching), and end-to-end MLOps orchestration using Azure ML Pipelines/MLflow plus Kubernetes/Argo with canary and rollback deployments.”

“Backend/platform engineer building HIPAA-compliant, real-time healthcare systems: owned a Python/Flask API layer for an AI-enabled patient engagement and risk scoring service, implemented PHI-safe logging and cross-service auditability, and delivered Kubernetes microservices via ArgoCD GitOps. Also has experience with Kafka streaming pipelines and hybrid cloud-to-on-prem migrations in regulated healthcare/fintech environments.”

“LLM/MLOps engineer who has shipped production systems for complaint intelligence and contact-center NLU, including LoRA/RLHF-tuned LLaMA models deployed on GKE with vLLM and Vertex AI batch pipelines to BigQuery. Demonstrates strong practical focus on hallucination control, data imbalance mitigation, and production monitoring (Langfuse) with regression testing and canary rollouts, plus experience orchestrating complex workflows with AWS Step Functions.”

“Full-stack developer who built a learning management web app end-to-end using React, Spring Boot, and MySQL, integrating APIs via Axios and validating/testing with Postman. Has experience handling data-heavy workloads (courses, quiz results) and improving performance with pagination, and is comfortable designing microservice-style endpoints with CI/CD considerations.”

“Summer SDE intern at AWS who built and deployed a column-lineage debugging tool for on-call engineers, using AWS Bedrock to parse SQL and generate a column DAG. Integrated the tool into an existing validation system and hardened it against real-world SQL format differences via flexible parsing and testing with queries from multiple upstream teams.”

“AI/full-stack engineer with experience delivering production-critical healthcare AI at Roche (digital pathology imaging platform using Microsoft Gigapath for cancer diagnosis) and building scalable LLM-backed products. Strong in designing async AI backends (Django/Celery/Postgres/Redis on GCP), reliability engineering (Datadog, incident response), and agent-style document analysis workflows with evaluation loops.”

“Backend-focused full-stack engineer who built an MVP at Neon AI for PhD students: a FastAPI backend integrating multiple cloud and local LLMs plus a RAG pipeline with session/identity management, designed to be modular and extensible across domains. Also has VMware experience debugging production issues and executing safe, API-compatible refactors with staged rollouts and strong security controls.”

“Associate Director in commercial technology leading Salesforce platform delivery (Sales Cloud + Health Cloud) for patient engagement and order management. Personally led secure integrations like Bartender Cloud label/barcode generation (PDF creation, encryption, malware scanning) and owned a major StreamSets-based Salesforce data sync incident triggered by a Salesforce region move, adding proactive monitoring and automated DR/failover. Experienced in scaling delivery via CI/CD, release cadence, and leading teams through architecture reviews, code reviews, and lead-to-cash automation.”

“Backend/data-systems SWE (2 years) who has built production ETL/streaming workflows (Kafka, Debezium, Elasticsearch) and troubleshot real SQL performance regressions caused by indexing/type issues. Also ships full-stack personal projects in Next.js App Router + TypeScript with Postgres, emphasizing reliability via constraints, idempotency, and strong observability (Grafana/Kibana).”

“Backend/microservices engineer with fintech loan-lifecycle experience operating low-latency (sub-250ms) services in production using Kafka, idempotent transaction design, and Datadog observability. Also built an end-to-end LLM chatbot (React + Flask) with a decoupled model integration layer (FLAN-T5 via Hugging Face) and has experience designing partner-facing REST APIs with OAuth2/JWT and Swagger documentation.”

“Built and shipped a production LLM-powered multi-agent system that autonomously generates and publishes YouTube videos end-to-end (trend discovery, script writing, image/caption generation, timestamped video assembly). Emphasizes production readiness with extensive automated testing, Redis/Postgres/TimescaleDB state orchestration, and Prometheus/Grafana monitoring, reporting ~100x faster content production and improved engagement/viewership.”

Swagat Adhikary

Screened

Junior Software Engineer specializing in LLM agents and FinTech platforms

Raleigh, NC1y exp

“AI/LLM engineer with Fidelity Investments experience who built and shipped a production GraphRAG system that augmented prompts with codebase context, improving business analyst efficiency by 15% and saving ~$3.5M annually. Strong in AWS EKS/Kubernetes/Helm and enterprise IAM/OIDC patterns (including cross-account S3 access), with experience mentoring interns and collaborating with non-technical leaders to extend AI pipelines (e.g., adding SQL functionality during MVP).”

Manaswini Gogineni

Screened

Junior Software Engineer specializing in cloud infrastructure and full-stack web development

San Francisco, CA2y exp

“Full-stack/platform engineer who has owned real-time analytics products end-to-end and built scalable TypeScript/React + Node.js systems using event-driven and microservices architectures (Kafka/RabbitMQ). Also created a widely adopted Go CLI that standardized AWS/Terraform provisioning across multiple teams, cutting environment setup from days to minutes through opinionated defaults, documentation, and cross-org partnerships.”

Gagan Reddy Konani

Screened

Mid-level Machine Learning Engineer specializing in LLMs and RAG for healthcare

Remote, USA2y exp

“AI Engineer (Medtronic) who deployed a production RAG-based clinical assistant grounded in curated biomedical literature (no patient-identifiable data). Deep hands-on experience orchestrating and hardening LLM workflows with LangChain/LangGraph, including stateful agentic flows, rigorous testing, and evaluation; reports a 72% accuracy improvement through retrieval enhancements (query rewriting, multi-query expansion, MMR reranking).”