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Vetted GraphQL Professionals
Pre-screened and vetted.
Executive CTO specializing in AI, cloud, and regulated SaaS platforms
Senior Full-Stack Software Engineer specializing in AI and cloud-native SaaS
Mid-level Full-Stack Java Developer specializing in cloud-native microservices
Senior Java Full-Stack Developer specializing in enterprise web apps and microservices
Senior UI Developer specializing in Angular and React enterprise web applications
Senior Java Full-Stack Developer specializing in cloud-native microservices and FinTech
Mid-level Full-Stack Developer specializing in React and Python (Django/FastAPI)
Senior Full-Stack Engineer specializing in cloud-native Java microservices and GenAI
Mid-level Backend Software Engineer specializing in data platforms and reporting
Senior Full-Stack Developer specializing in cloud-native microservices (AWS)
Satya Kumar Itekela
Senior Frontend Engineer specializing in high-performance React/Next.js web apps
“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.”
KrishnaVarshith Pabbisetty
Mid-level Backend Software Engineer specializing in Spring Boot, microservices, and cloud-native AI
“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.”
Rafael Ortega
Senior Full-Stack & AI Engineer specializing in LLM integrations and cloud-native systems
“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.”
Sai Haritha Sardena
Mid-level Full-Stack Software Engineer specializing in web, cloud, and AI/ML
“Software engineer with experience at Wipro and Tylmen Tech owning customer-facing onboarding and real-time features end-to-end (React + Spring Boot) and building TypeScript/React apps backed by Node.js microservices (MongoDB, RabbitMQ). Strong in production reliability and fast iteration: feature-flagged rollouts, idempotent APIs/consumers, DLQs, and SLO-driven incident tooling, including an internal QA & release dashboard adopted by engineering and support teams.”
Senior Software Engineer specializing in backend microservices and data platforms
Fatema Tuzzohra
Senior Full-Stack Java Developer specializing in banking and healthcare platforms
“Enterprise-focused full-stack/backend engineer with hands-on experience shipping a production LLM-powered document analysis platform for internal operations teams. They combine cloud-native microservices expertise across AWS/Azure with practical agent-system design, including async orchestration, model routing, observability, and safeguards for hallucinations, failures, and human-review fallbacks.”
Nithin Reddy
Mid-Level Software Development Engineer specializing in distributed systems and event-driven architectures
“Built and maintained an internal JavaScript/React real-time event monitoring UI used by multiple Goldman Sachs teams (e.g., Private Wealth Management and Bulk Trading Systems). Focused on scaling performance under hundreds of events/sec—using profiling, memoization, batching, and debouncing—and paired it with strong internal documentation and disciplined incident diagnosis via synthetic load testing and logs/metrics.”