Vetted React Professionals

rhema junapudi

Screened

Mid-level Full-Stack Developer specializing in cloud-native healthcare and insurance platforms

Albany, OR3y exp

“Full-stack engineer with healthcare and claims-domain experience who has shipped AI-assisted documentation and fraud-scoring systems using Angular, ASP.NET Core, OpenAI APIs, and ML tooling. Stands out for building compliance-aware, traceable agentic architectures with graceful degradation, and for a nuanced understanding of AI failure modes in production.”

Mounika Ravali Vundemodalu

Screened

Junior Front-End Developer specializing in insurance web applications

San Jose, USA3y exp

“Full-stack developer who uses AI selectively for refactoring, regex, API typing, and test scaffolding while maintaining strict manual review for security, correctness, and architectural fit. Recently shipped insurance-related features such as policy issuance and reinstatement, and demonstrated hands-on debugging of async React issues by identifying duplicate fetches in DevTools and fixing them with AbortController.”

“Early-career engineer who built and launched a zero-to-one AI-driven approval workflow at SDSU that is used daily by roughly 2,000 university users. They owned the system end-to-end—from FastAPI/PostgreSQL backend to React UI—and showed strong judgment around LLM reliability, using a two-step pipeline, validation checks, and human-review fallbacks to cut manual processing time by about 80%.”

Yogita Adari

Screened

Mid-level AI Engineer specializing in generative and multimodal systems

San Francisco, CA4y exp

“Built and productionized an agentic LLM automation system for an insurance client to determine medication eligibility, using prompt-chaining plus a RAG pipeline over policy rules and deploying on AWS (Lambda/Step Functions, Bedrock) with a serverless architecture. Addressed major data/schema mismatch issues via a semantic matching pipeline and validated performance through human agreement scoring, A/B testing, KPI monitoring, and confidence-based human-in-the-loop review.”

Bhagyashree Patil

Screened

Intern Applied AI Engineer specializing in LLM systems and data engineering

Los Angeles, CA1y exp

“Full-stack engineer with hands-on production experience across both traditional SaaS and LLM-powered support tooling. They owned a real-time ecommerce order tracking dashboard that improved support response times by 40%, and helped ship an AI support assistant using the OpenAI GPT API that cut ticket handling time by 30% through strong prompt design, retrieval grounding, validation, and human-in-the-loop safeguards.”

Pallavi Hogalagere Anandareddy

Screened

Mid Software Engineer specializing in backend systems and FinTech

Tampa, FL4y exp

“Backend/full-stack engineer with experience spanning banking implementations at FIS and hands-on product building in React, TypeScript, Node.js, SQL, and Python. Stands out for owning six bank go-lives end-to-end, building an AI resume evaluator, and proactively automating manual operations workflows to reduce errors and improve consistency.”

“Built an AI-powered document extraction system at Copart that moved from initial POC to rollout across multiple yards, with customer-driven iteration including batch processing. Has hands-on full-stack experience spanning Java/Spring Boot backends, React frontends, and LLM-based workflows, with attention to reliability through multi-LLM fallback, retries, and post-processing.”

“Backend-leaning full-stack engineer who has shipped both enterprise workflow software and AI-powered document intelligence products. Stands out for combining practical product judgment with strong production debugging skills across Spring Boot, GraphQL, FastAPI, vector search, and RAG systems, and for improving adoption by making AI search experiences intuitive for non-technical users.”

Raj Kalantri

Screened

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

Kolkata, India1y exp

“Built a sophisticated AI career counselor as a full-stack web app for early-career students, integrating React, Flask, Pinecone, and LLM inference into a stateful conversational product. Stands out for combining hands-on debugging of retrieval/embedding pipelines with strong browser-performance instincts and pragmatic UX iteration based on real user testing.”

“Full-stack and AI product engineer who has shipped both operational SaaS and LLM-powered research tools to production. Built a dispatch optimization system at Quickflora that cut manual effort by ~50%, and also developed grounded RAG and agentic systems using tools like LlamaIndex, Gemini, pgvector, and FastAPI with a strong emphasis on citations, reliability, and practical user workflows.”

“New grad software engineer with hands-on experience building production full-stack analytics infrastructure during a Swiftwise AI internship and independently shipping AI products. Stands out for combining strong TypeScript/React/backend fundamentals with practical RAG and agent-building experience, including a poker coaching assistant built solo from ingestion and retrieval through prompt tuning and evaluation.”

“Frontend-focused engineer building production-grade, browser-based AI products, including a live clinical decision support platform for Stanford clinicians. Stands out for combining sophisticated React/TypeScript streaming UI architecture with strong browser performance expertise, including Core Web Vitals and rendering-pipeline optimization.”

“Built a real-time telemedicine clinician dashboard and iterated post-launch by diagnosing lag via logs/metrics and optimizing DB queries/sync logic. Also shipped a production internal RAG knowledge assistant for support teams, including embeddings/vector DB, citation-only answers with abstention thresholds, and an eval loop driven by real ticket data that improved accuracy through chunking/overlap and batching optimizations.”

“Full-stack engineer with strong banking/financial-platform experience, spanning React/Angular frontends, Spring Boot microservices, Kafka, and secure API design. They’ve built high-volume transaction systems handling 40,000+ daily requests and also owned an AI-driven search/recommendation feature that improved search relevance by 20% and adoption by 12%.”

“Backend engineer who built and launched a warehouse locations/inventory microservice for Walmart, supporting a new product rollout with on-call war-room ownership and now running across all US distribution centers. Emphasizes reliability and correctness (background syncs, 2PC concepts, alerting) plus design-first API development in Python/FastAPI with OAuth/JWT and RBAC, and has led staged legacy-to-microservice migrations with continuous data integrity verification.”

“Backend engineer focused on Node.js (Express/Fastify) and MongoDB who designed a multi-stage bill-approval workflow system for a manufacturing company, emphasizing RBAC, auditability, and scalability across multiple factory units. Also improved system robustness by catching a MongoDB connection leak in an Excise department project and has experience executing low-risk, incremental backend refactors with monitoring and rollback.”

“Backend/data platform engineer who has built and operated a cloud-native media ingestion/processing platform in Python (Django/DRF, FastAPI) with Kafka, Postgres, and Redis, emphasizing multi-tenant security and reliability. Delivered AWS production systems combining EKS and Lambda with Terraform + GitHub Actions/Helm, and built Glue-based ETL pipelines with strong schema-evolution and data-quality practices; also modernized SAS analytics into Python on AWS. Seeking fully remote roles with a $120K–$140K base range.”

“Backend engineer with hands-on experience modernizing high-volume transactional systems by decomposing monoliths into Spring Boot microservices on AWS, using Kafka for async workflows and Redis/SQL tuning for latency. Has built Python/FastAPI services with strong API contracts and production-grade security (OAuth2/JWT, RBAC, row-level security), and proactively hardened payment flows against race conditions and double-charging via idempotency.”

“Entry-level software engineer who built an agentic AI backend in Python/FastAPI, including APIs for conversation history retrieval and user data storage, and worked through async/concurrency challenges for multiple agents querying simultaneously. Also has practical AWS experience using S3 for static hosting with Lambda and RDS for backend/data access.”

“Backend/data platform engineer who owned end-to-end production services for a fleet analytics/GenAI platform, spanning FastAPI microservices on Kubernetes and AWS (EKS + Lambda) event-driven workloads. Strong in reliability/observability (OpenTelemetry, circuit breakers, idempotency), data pipelines (Glue/Airflow/Snowflake), and measurable performance/cost wins (SQL 10s to <800ms P95; ~30% compute cost reduction).”

“Frontend engineer who has led React + TypeScript products end-to-end, emphasizing scalable architecture (custom hooks, compound components) and quality via strict typing, CI, and code review standards. Experienced shipping complex multi-step workflows with strong performance practices (virtualization, code splitting, RUM) and de-risked rollouts using feature flags, monitoring, and rapid iteration in collaboration with product/design.”

“AI/LLM practitioner at iFrog Marketing Solutions who drove a RAG chatbot from prototype to production in a legacy, AI-resistant environment by validating customer needs and building a business case. Implemented production-grade LLM practices (CI/CD eval gating, rollbacks, prompt/context engineering) and led internal workshops to bring non-AI-native developers up to speed while partnering with sales on tailored demos to drive adoption.”

“Backend/web API engineer with hands-on experience deploying .NET Core APIs to Azure App Service and stabilizing production systems through disciplined log-driven troubleshooting, environment configuration management, and SQL performance tuning (execution plans, query rewrites, indexing). Has also debugged cross-layer incidents involving DB locks and network latency, and modifies Python/XML automation scripts to meet customer-specific requirements while improving logging and performance.”

“LLM/agent builder who has shipped production AI systems in the wellness space, including an LLM-powered food tracking product used by 5000+ users and a voice/call-routing onboarding workflow using LangGraph/LangChain with LiveKit and Twilio. Strong focus on practical reliability work: latency reduction, retrieval/embedding tuning, and CI-driven evaluation with simulations and metrics.”