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Vetted Full Stack Developers in Remote
Pre-screened and vetted in Remote.
Senior Full-Stack Engineer specializing in AI, Web3, and scalable web platforms
Bhagya Sunkara
Mid-level Full-Stack Developer specializing in cloud-native backend services and real-time data platforms
“Backend/data engineering candidate with Netflix experience designing and migrating analytics platforms from batch to real-time streaming (Kafka/Flink) across AWS and GCP. Delivered measurable improvements (40% lower data delay, 99.9% accuracy) using phased rollouts, automated data validation (Great Expectations), and strong observability (Prometheus/Grafana), and proactively hardened pipelines with idempotency to prevent duplicate Kafka processing.”
Yeshwanth Sai Pala
Mid-level Full-Stack Developer specializing in cloud microservices and AI-driven FinTech
“Stripe engineer who shipped an end-to-end merchant fraud insights dashboard, spanning Spring Boot/Kafka risk-scoring services and a React+TypeScript UI. Focused on low-latency, high-volume transaction processing and production operations on AWS (EKS/CloudWatch), including handling a real traffic-spike latency incident via query optimization, indexing, and rate limiting.”
Junior Full-Stack Developer specializing in cloud-native microservices
Mid-level Full-Stack Developer specializing in AI-driven FinTech platforms
Mid-level Full-Stack Developer specializing in cloud-native FinTech and Healthcare IT
Sarath Dunga
Mid-level Full-Stack Developer specializing in cloud microservices and AI/ML integration
“Full-stack engineer (~3 years) with eBay production experience building and operating high-scale, event-driven Python microservices for order processing and AI-powered recommendations (Kafka/Redis/FastAPI on AWS with Prometheus/Grafana). Also delivered polished React+TypeScript analytics dashboards and designed high-concurrency PostgreSQL schemas with significant latency reductions. Recently built AI-agent orchestration and an interactive node-based requirements dashboard for Siemens Polarion via MCP servers, improving user interaction by ~17.8%+.”
Devika gade
Mid-level Full-Stack Developer specializing in FinTech and cloud-native applications
“Full stack developer with strong implementation ownership across cloud deployments, integrations, and AI-powered support automation. They have put LLM/RAG workflows into production with measurable impact—cutting first response time by nearly 40%—and show unusual depth in debugging non-deterministic AI incidents, improving observability, and turning messy document inputs into reliable API-driven pipelines.”
Mid-level Full-Stack Developer specializing in .NET, Java microservices, and React
Mid-level Full-Stack Developer specializing in cloud-native and AI-powered platforms
Mid-level Full-Stack & AI Engineer specializing in backend systems and Voice AI
Mid-level Full-Stack Developer specializing in FinTech payments and microservices
Mid-level Full-Stack Developer specializing in FinTech and fraud detection
Jugal Rayala
Mid-level Full-Stack Developer specializing in AI-powered cloud applications
“Full-stack engineer who has owned customer-facing AI recommendation and analytics dashboards end-to-end (backend APIs/data processing through React UI, deployment, and monitoring). Demonstrates strong systems thinking around scaling microservices—using observability, caching, async workflows, and resilience patterns—and also built an internal ops dashboard that became the default tool for on-call incident reviews.”
Senior Full-Stack Engineer specializing in Python, cloud-native systems, and LLM applications
Sindhu Gunti
Mid-level Full-Stack Java Developer specializing in cloud microservices and AI-driven platforms
“Software engineer with Intuit experience shipping an end-to-end real-time financial insights product on AWS, using event-driven architecture with Kafka and Spark Streaming to process millions of records with low latency. Also delivers customer-facing React + TypeScript dashboards and has hands-on production operations experience, including resolving a database scaling incident via read replicas, query tuning, and connection pooling.”