Vetted React Testing Library Professionals

“Built and shipped a production generative-AI recipe feature on AWS serverless (Lambda + Bedrock), evolving it post-launch from fully AI-generated outputs to user-guided structured generation based on real usage patterns and system metrics. Emphasizes reliability via prompt constraints plus deterministic validation, with automated/human eval loops and CloudWatch-based observability to manage latency, cost, and output consistency.”

“Product-minded software engineer with experience shipping AI-powered financial insights (spend forecasting, cashflow, credit optimization) and building real-time analytics systems using React/TypeScript and FastAPI. Has designed microservices with RabbitMQ/gRPC and strong observability (Prometheus/Grafana/OpenTelemetry), and also built an internal Figma plugin adopted by designers that reduced export time by 50%.”

Byron Pineda

Screened

Staff/Lead Data Scientist specializing in Generative AI, NLP/LLMs, and MLOps

Pascagoula, MS10y exp

“Lead Data Scientist (10+ years) with recent work in healthcare data: built production pipelines that unify EHR, genomics, and clinical notes using NLP (spaCy/BERT/BioBERT) and scalable Spark-based processing. Also led development of domain-specific LLM/NLP systems for chatbots and semantic search, deploying models via FastAPI/Flask and improving retrieval with FAISS-backed, fine-tuned clinical embeddings and RAG-style workflows.”

“Engineer with hands-on experience using AI coding tools like Claude Code and Augment in daily development, combined with real-world distributed systems work at Arista Networks. Built or worked on a Go-based platform that separated RBAC, audit logging, and provisioning into asynchronously communicating services, enabling scalable and independently evolving access-control workflows.”

“Built and owned end-to-end production systems for a healthcare platform, including a predictive task recommendation feature (React + FastAPI + ML on AWS ECS) that cut backlog 20% and saved coordinators ~10 hours/week. Also productionized an AI-native RAG system (vector DB + LLM) delivering 40% faster query resolution, and led phased modernization of a monolithic FastAPI service into async microservices using feature flags and canary releases.”

Arun Gampala

Screened

Mid-level Full-Stack Developer specializing in MERN and AWS microservices

TX, USA4y exp

“Backend engineer with experience at MetLife and Amazon focused on security and control for internal and customer-facing services. Emphasizes contract-first Python/FastAPI APIs with strong auth (JWT + RBAC/claims), data-layer isolation (RLS/tenant scoping), and reliability practices like incremental refactors, rollback planning, and idempotency to handle retry-driven failure modes.”

Vasudha Prerepa

Screened

Mid-Level Java Full-Stack Developer specializing in cloud-native microservices

5y exp

“QA/validation-focused engineer with experience at Meta testing an ML+LLM content classification/summarization system, including production-vs-test behavior gaps. Built automated E2E validation and drift monitoring (PSI, KL divergence, embedding cosine similarity) run daily/multiple times per day and gated via CI. Also implemented Jenkins-orchestrated Selenium/API test suites in Docker at Capgemini and partnered with a business analyst to convert business rules into automated AI-driven validation checks.”

“Data/ML backend engineer with hands-on production experience spanning RAG services (LlamaIndex/OpenAI) and AWS data platforms. Has delivered Terraform-managed AWS architectures (Lambda + ECS Fargate) with secure secrets handling, built Glue-to-Redshift ETL with schema evolution controls, modernized SAS reporting into Python microservices, and achieved major Redshift query speedups (2+ hours to under 15 minutes).”

Akshaya Rajagopalan

Screened

Mid-level Full-Stack Software Engineer specializing in cloud SaaS and accessible web apps

Waterloo, Canada6y exp

“Frontend engineer who leads end-to-end delivery of complex workflow-driven React + TypeScript products on top of Rails/GraphQL backends, with a strong emphasis on typed API contracts, scalable architecture, and automated quality gates. Shipped major features (e.g., inventory reservation at Jobber) using feature-flagged rollouts, close QA collaboration, and performance-focused iteration.”