.png){kind=logo}
Vetted Spring Framework Professionals
Pre-screened and vetted.
Director-level Engineering Manager / Java Architect specializing in cloud-native payments microservices
Junior Software Engineer specializing in FinTech and full-stack development
Senior Business Analyst / QA Lead specializing in cloud, security, and enterprise testing
Junior QA Analyst specializing in telecom testing and data-driven insights
Mid-Level Full-Stack Java Developer specializing in Spring Boot, React, and AWS
Junior Software Engineer specializing in backend systems, QA automation, and AI/ML
Executive CTO and venture builder specializing in AI-native SaaS and consulting
SAI PRASHANTH
Mid-level Full-Stack Software Engineer specializing in cloud-native microservices and FinTech
“Software engineer/product-minded builder who owns customer-facing products end-to-end and ships in 1–2 week increments using CI/CD, automated testing, and feature flags. Built a TypeScript/React/Node platform that cut page load times by 40% and scaled to 3x concurrent users, and designed RabbitMQ-based microservices with Prometheus/Grafana monitoring. Also delivered an internal real-time support analytics dashboard that reduced response times by 30%.”
Shubh Dholakiya
Mid-Level Full-Stack Software Developer specializing in Java/Spring microservices and cloud
“Backend engineer who owned and shipped a campaign analytics API (FastAPI/Postgres/Redis/Celery) with ingestion from Instagram/YouTube, JWT auth, tests, and Docker deployment; improved performance from >1s to <150ms using precomputed aggregates and composite indexes. Experienced with Kubernetes GitOps using GitHub Actions + ArgoCD (zero-downtime rollouts, one-click rollbacks), Prometheus/Grafana observability, hybrid cloud-to-on-prem migrations, and real-time notification streaming via Redis Pub/Sub + WebSockets.”
Mid-Level Software Engineer specializing in Java microservices and cloud-native AWS development
Mid-level AI/ML Engineer specializing in cloud-native data pipelines and RAG systems
Mid-level Full-Stack Software Engineer specializing in Java/Spring Boot and React
Mid-level Full-Stack Software Engineer specializing in cloud-native microservices
Mid-Level Frontend/Full-Stack Engineer specializing in web apps and FinTech
Junior Software Engineer specializing in backend, microservices, and cloud
Carlos José Aracena Sos
Senior Full-Stack Software Engineer specializing in React/Angular and FinTech/Telecom web apps
“Frontend engineer with telecom and fintech/banking experience in the Dominican Republic, building React/Angular apps with authentication and real-time data. Notable wins include optimizing an Oracle-backed workflow from ~10s to ~2s via DB indexing plus React memoization, and shipping a SignalR real-time transaction table in one week (award-winning) with strong unit-test-driven QA outcomes.”
Abhishek Namdev Sawant
Mid-Level Backend Software Engineer specializing in Java microservices and cloud platforms
“Backend/platform engineer with payments and insurance domain experience (Cognizant), owning high-volume production systems end-to-end. Shipped a Spring Boot payment tokenization service with strong observability and phased migration that cut transaction latency ~30% and improved payment efficiency ~25%. Also productionized an ML-driven financial health/risk analytics pipeline with near real-time dashboards across 70+ schools, emphasizing interpretability, data quality, and drift monitoring.”
Haytham Nasri
Software Engineering Manager specializing in cloud-native mobility and enterprise Java
“Backend/architecture-focused player-coach who designed and built two partner-facing data APIs using a contract-first (OpenAPI) governance model in Java (Quarkus/Spring Boot). Strong in technical standards, incident response, and delivery systems—improved reliability under peak load (DB/index fix) and sped up delivery by streamlining PR/review processes while maintaining production stability.”
Ravali Yerrapothu
Senior Machine Learning Engineer specializing in LLMs, computer vision, and cloud AI
“Healthcare-focused ML/AI engineer who has built clinical note summarization and medical image annotation solutions using LLMs, RAG, and multimodal models. They combine experimentation across major model providers with practical production concerns like monitoring, drift detection, and latency/cost tradeoffs, and also earned 2nd place in a Google hackathon for a medical AI assistant.”