Vetted Software Engineers

“Frontend/full-stack engineer (~5 years) who led large-scale React + TypeScript products including a Shutterstock-like media platform with private, invite-based content and SSO-style auth redirection. Strong in performance for media-heavy experiences (thumbnails, CDN, caching, chunking) and in quality systems (Vitest, E2E, CI gates, QA/staging). Also built a complex Figma/PowerPoint-like editor with custom state management and undo/redo history tracking.”

“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.”

“Backend engineer who built and scaled AmazonProAI, a multi-tenant SaaS platform for Amazon sellers, using a modular Django/DRF monolith with strict seller-level isolation and security controls. Led a controlled SQLite-to-PostgreSQL migration and hardened bulk Excel ingestion with idempotency and data integrity constraints to prevent duplicate metrics and noisy alerts while keeping the system ready for future service extraction.”

“Early-career/learning-stage candidate focused on LLM systems; has not yet built or deployed production AI applications but is actively learning orchestration (Microsoft Semantic Kernel) and core patterns like RAG, embeddings, and model selection based on business requirements.”

Fabricio Anchundia

Screened

Junior Backend/Full-Stack Developer specializing in .NET and microservices

Manabí, Ecuador

“Frontend engineer focused on scalable React + TypeScript applications, with repeated examples of modular architecture, reusable component systems (forms/tables), and keeping business logic out of UI components. Has experience improving maintainability/performance in existing codebases through gradual refactoring, and shipping key features under tight deadlines using MVP scoping and post-launch iteration based on user feedback.”

“AI/LLM engineer who has owned end-to-end production delivery of multi-agent RAG systems on Azure (React + FastAPI + data pipelines + Terraform), including rigorous evaluation/monitoring and reliability guardrails. Shipped an AI-driven observability root-cause analysis assistant that reduced MTTR ~30%, cut alert noise ~20%, and reached ~70% adoption in the first month; also built a clinical document Q&A system with citations and compliance-oriented controls.”