Vetted Cursor (AI Code Editor) Professionals

“Full-stack Java/Spring Boot + SPA (Angular/React) engineer who has delivered products end-to-end, including a financial management portal for SMBs and a high-traffic golf/hotel reservation platform. Notably migrated a manually deployed system to AWS (EC2/RDS/S3/CloudFront) with Jenkins CI/CD and has hands-on React performance optimization experience for LG webOS TV apps.”

“Frontend-focused developer who built a chess web product end-to-end (including an admin panel) using Next.js with SSR. Emphasizes reusable component architecture, performance optimization (bundle-size reduction, lazy loading), and quality practices like Lighthouse audits and Playwright E2E testing, with hands-on experience handling Next.js hydration pitfalls.”

“Full-stack engineer who built an end-to-end healthcare logistics application on a Microsoft services stack, delivering Spring Boot microservices and a responsive React frontend. Strong focus on production concerns—API performance tuning (indexing/caching/pagination) and microservice reliability (timeouts/retries/circuit breakers)—with Dockerized deployments and standardized Jenkins CI/CD.”

“Frontend engineer (~4 years) who led an admin dashboard for potato farmers end-to-end, integrating real-time and historical weather data to drive disease-severity predictions. Built scientifically validated calculation pipelines (FAO Penman-Monteith, late blight models), rigorous Cypress/unit testing, and print/PDF-ready reporting, while optimizing performance and API load with debouncing, caching, and feature-flagged rollouts.”

“Full-stack engineer who built an end-to-end Next.js + Flask admin platform for Ciocca S.p.a., now used daily by 100+ employees for operations and warehouse workflows. Strong in performance tuning (API redesign, query optimization), secure RBAC, background job offloading, and integrating AI-powered internal tools that automate reporting and reduce manual workload.”

“Built and shipped a production-grade RAG-powered news summarization and Q&A product, tackling real-world issues like retrieval drift, hallucinations, latency, and autoscaling deployment (Docker + FastAPI + Streamlit Cloud). Experienced in end-to-end ML/LLM workflow automation using Airflow, Kubeflow Pipelines, and MLflow, and has demonstrated business impact (40% inference precision improvement) through close collaboration with non-technical stakeholders at Evoastra Ventures.”