.png){kind=logo}
Vetted Amazon API Gateway Professionals
Pre-screened and vetted.
Senior Full-Stack Java Developer specializing in cloud-native microservices (FinTech/Healthcare)
Mid-Level Software Engineer specializing in cloud-native microservices for healthcare and finance
Senior Full-Stack Java Engineer specializing in microservices and cloud-native systems
Mid-level Full-Stack Developer specializing in Java/Spring Boot, React, and cloud-native microservices
Mid-level Data Scientist specializing in GenAI, NLP, and recommendation systems
Mid-level AI/ML Engineer specializing in NLP, MLOps, and scalable ML platforms
Junior Software Engineer specializing in cloud-native microservices
Senior Software Engineer specializing in event-driven microservices and GenAI for payments
Mid-Level Software Engineer specializing in Java/Spring microservices for FinTech
Senior DevOps & Cloud Engineer specializing in AWS, Kubernetes, Terraform, and DevSecOps
Senior UI Developer specializing in Angular and React enterprise web applications
Senior DevOps & Site Reliability Engineer specializing in multi-cloud CI/CD and IaC
Senior Full-Stack Engineer specializing in cloud-native Java microservices and GenAI
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.”
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.”
Pavan Kalyan
Mid-level AI Engineer specializing in GenAI agents and RAG for IT operations
“Built and operates a production LLM agent for enterprise IT operations that triages and drafts resolutions for high-volume ServiceNow tickets using LangChain + RAG (Pinecone/pgvector) and AWS Bedrock/OpenAI. Emphasizes reliability with schema-validated stages, offline eval datasets from real tickets, and CloudWatch-driven monitoring/guardrails; system scales to 40K+ tickets/month and cut resolution time ~28%.”
Senior Software Engineer specializing in backend microservices and data platforms
Manasa Pantra
Junior Software Engineer specializing in AI, LLM systems, and full-stack development
“Product-focused full-stack engineer at startup (Zippy) who shipped a production multi-agent AI system for restaurant operations plus payments workflows. Built end-to-end: RAG grounded on a Notion knowledge base, structured function-calling task routing, FastAPI/JWT multi-tenant backend, and a polished React+TypeScript owner dashboard. Has real production incident experience (duplicate Stripe webhooks) and reports ~94% task-routing accuracy under load.”
Vishesh Sharma
Senior Full-Stack Java Developer specializing in cloud-native microservices and real-time web apps
“Full-stack engineer/product owner who built and scaled a customer-facing job application portal (Skillbridge) using TypeScript/React and Spring Boot/MongoDB, optimizing search performance with indexing, caching (Redis), and payload/lazy-loading improvements. Also built an internal AI-driven analytics dashboard for Salesforce operations using OpenAI sentiment analysis, achieving 70% reduction in manual analysis and driving adoption through demos and iterative feedback.”