Vetted GraphQL Professionals

“Internship experience at Intuit building an LLM-grounded QA system for internal microservice data across 100+ microservices, using a graph database approach (evaluated Neo4j and selected AWS Neptune for production alignment). Also has UC Berkeley research experience (including work with Prof. Dawn Song / Berkeley Eye Research Lab) and cross-functional collaboration with bioinformatics/biology teams to deploy software systems on research servers.”

“Series C (Sigma Computing) full-stack engineer/intern who shipped production features across React/TypeScript and GraphQL, including a Recents/workbook activity reliability improvement that handled unsaved “exploration” events via deterministic backend updates. Emphasizes production quality through Jest/Cypress coverage and feature-flagged staged rollouts, and is recognized for UX-focused improvements (fast, accurate filtering at scale) and proactive cross-functional ownership.”

KARTHIKBABU VADLOORI

Screened

Mid-level Full-Stack Developer specializing in Spring Boot, React, and cloud microservices

San Francisco, CA5y exp

“Backend engineer with experience at Meta and Accenture building regulated-data systems (healthcare/financial) using Python/Flask and Postgres. Has scaled high-throughput services to millions of daily requests, delivering measurable latency wins (~40% API latency reduction; ~35% faster DB-backed endpoints), and has productionized ML inference services using Docker/Kubernetes and AWS (ECS/SageMaker).”

“Engineering lead/player-coach at Sam's Club who drove major React Native Member Desk Returns changes (including Vision Center insurance refund flows) by coordinating Product/UX, QA, and API teams and leading a 3-engineer squad. Also led an e-commerce mobile OAuth migration, evolving a login experience from vanilla JS to a React SPA as security requirements (MFA, reCAPTCHA) emerged, and improved production reliability via better request flagging and analytics/log instrumentation.”

Jordan Lineberry

Screened

Senior Full-Stack Engineer specializing in Healthcare SaaS and supply chain systems

New York, NY11y exp

“Backend engineer with healthcare platform experience at Thirty Madison, combining Django for secure, data-heavy core services with FastAPI async microservices for real-time patient monitoring. Led Kubernetes migration with Istio service mesh, autoscaling (HPA), and stateful storage patterns, and implemented GitOps CI/CD using ArgoCD. Also built real-time Kafka streaming pipelines with reliability patterns like idempotent producers and offset management.”

“Engineer who uses AI thoughtfully as a productivity multiplier rather than a crutch, with hands-on experience applying agent-based workflows to coding, debugging, documentation, and testing. Particularly strong in rapid backend and data-processing development, with a clear emphasis on validation, architecture, and scalability.”

“AI/LLM-focused engineer with hands-on experience building RAG pipelines, prompt engineering workflows, and multi-agent systems using tools like LangChain. Stands out for combining AI-assisted development with production-grade validation and for leading the architecture/orchestration of agent-based recommendation systems that improved response time, accuracy, and scalability.”

“Backend/data engineer with Optum healthcare claims domain experience building high-reliability Python microservices (FastAPI/Kafka/Postgres) and AWS data platforms (EKS, Glue, Redshift). Demonstrated strong production ownership: fixed duplicate Kafka processing via transactional outbox/idempotency, scaled to millions of daily events, and delivered major SQL performance gains (40+ min to <5 min, ~60% CPU reduction). Seeking remote-only work; targets $130k base.”

“Backend/data engineer who has owned production Python APIs and high-throughput async workflows on AWS (FastAPI, Docker, ECS/EKS/Lambda) with mature reliability practices like idempotency, bounded retries, circuit breakers, and strong observability. Also built AWS Glue ETL into an S3/Redshift lakehouse and modernized legacy batch systems via parallel-run parity testing and feature-flagged migrations, including a SQL tuning win cutting a multi-minute query to under 10 seconds.”

“Built a production RAG-based "knowledge copilot" for support/ops using LangChain/LangGraph, implementing the full pipeline (ingestion, chunking, embeddings, vector DB retrieval/rerank, guarded generation with citations) and operating it as monitored microservices with CI/CD. Also designed an event-driven, streaming backend for real-time inventory ordering predictions that reduced stockouts by 25%, and has hands-on incident response experience stabilizing LLM API latency/5xx spikes using Datadog/APM and resilience patterns.”