Vetted GraphQL Professionals

Pre-screened and vetted.

KG

Senior Full-Stack Engineer specializing in scalable web, mobile, and cloud platforms

New York, NY7y exp
TalentsnipeColumbia University
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JS

Mid-level Full-Stack Engineer specializing in frontend web applications and FinTech

Ridgefield Park, NJ4y exp
NexienBoston College
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HR

Mid-level Python Developer specializing in cloud-native FinTech platforms

Texas, USA4y exp
IntuitAvila University
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AM

Mid-level Full-Stack Engineer specializing in iOS, web, and distributed systems

Chicago, IL3y exp
University of ChicagoUniversity of Chicago
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NK

Senior Full-Stack Java Developer specializing in Financial Services and Healthcare IT

Chicago, IL6y exp
Bank of AmericaPurdue University Northwest
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GB

Senior Java Full-Stack Developer specializing in enterprise web apps and microservices

Charlotte, NC15y exp
Bank of America
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AD

Senior UI Developer specializing in Angular and React enterprise web applications

Mckinney, TX8y exp
PNC
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FK

Senior Java Full-Stack Developer specializing in cloud-native microservices and FinTech

Bristow, VA9y exp
Bank of America
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PA

Mid-level Full-Stack Developer specializing in React and Python (Django/FastAPI)

Toronto, Canada6y exp
The Home Depot
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KK

Senior Full-Stack Engineer specializing in cloud-native Java microservices and GenAI

New York, NY7y exp
JPMorgan Chase
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SP

Mid-level Full-Stack Engineer specializing in AI-powered enterprise applications

US6y exp
BioNTech
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SK

Senior Full-Stack Java Developer specializing in microservices and Healthcare/FinTech systems

Seattle, WA5y exp
T-Mobile
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AM

Mid-level Backend Software Engineer specializing in data platforms and reporting

Pleasanton, CA5y exp
Workday
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TS

Senior Full-Stack Developer specializing in cloud-native microservices (AWS)

Irving, TX8y exp
U.S. Bank
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AS

Executive engineering leader specializing in AI, FinTech, and cloud platforms

San Carlos, CA25y exp
Presto Phoenix, Inc
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DW

Senior Front-End Engineer specializing in React platforms and web experiences

New York City, NY8y exp
Rockstar Games
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Satya Kumar Itekela - Senior Frontend Engineer specializing in high-performance React/Next.js web apps in Remote, Canada

Satya Kumar Itekela

Screened ReferencesStrong rec.

Senior Frontend Engineer specializing in high-performance React/Next.js web apps

Remote, Canada8y exp
AutodeskDalhousie University

Frontend engineer with experience at Autodesk and Quantify, leading and scaling Next.js/React + TypeScript products from architecture through QA. Strong focus on performance (Core Web Vitals, ISR, caching/CDN) and real-time interfaces (WebSockets, Chart.js/D3), with measurable wins like 30–40% bundle reduction and ~60% less data overfetching using GraphQL/Apollo.

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KP

KrishnaVarshith Pabbisetty

Screened ReferencesStrong rec.

Mid-level Backend Software Engineer specializing in Spring Boot, microservices, and cloud-native AI

Bay Area, CA4y exp
TemenosSan José State University

Backend engineer with experience modernizing a large-scale procurement platform at Jio Platforms by breaking a monolith serving 35 business units into Spring Boot microservices, improving uptime and cutting report latency ~30%. Also built high-concurrency FastAPI systems (200ms at ~500 concurrent users) with strong security (JWT/OAuth2/RBAC), event-driven Kafka integrations, and reliability patterns like exactly-once delivery for ~1M monthly triggers.

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RO

Rafael Ortega

Screened ReferencesStrong rec.

Senior Full-Stack & AI Engineer specializing in LLM integrations and cloud-native systems

Remote (Texas)8y exp
BlocUnitedNew Mexico Tech

Backend/data engineer with hands-on production experience building FastAPI Python APIs and AWS-native platforms (Lambda/API Gateway, SQS, ECS Fargate) with Terraform + GitHub Actions CI/CD and strong reliability practices (JWT/RBAC, retries/timeouts, structured errors/logging). Also built AWS Glue ETL pipelines (S3/RDS to curated S3/Athena) with schema evolution and data quality controls, modernized legacy processing via parallel-run validation and phased cutovers, and has demonstrated SQL tuning impact (seconds to <200ms) plus incident ownership for batch pipeline SLAs.

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