Vetted Unit Testing Professionals

Sudarshan Naicker

Screened

Entry-Level Software Engineering Student specializing in Full-Stack Web Development and ML

Calgary, Canada1y exp

“Frontend-focused builder with multiple end-to-end academic product builds (event manager, niche social media app, airline booking site, AI-based obituary generator). Experienced in React/Next.js with an emphasis on scalable component architecture, pragmatic state management/performance, and strong collaboration with design via Figma; has shipped complex user flows like real-time chat, booking/seat selection, auth, and media upload under tight timelines.”

KhaingWutyi Win

Screened

Junior Frontend Developer specializing in React and TypeScript

Yangon, Myanmar3y exp

“Frontend engineer who led end-to-end development of a role-based React + TypeScript dashboard (hotel booking management) with deep linking and complex workflows. Emphasizes scalable architecture, strict quality gates (TypeScript strict mode, ESLint, testing, code review), and rigorous state separation (server/URL/local) to prevent stale or mixed-up UI. Proven at performance-focused refactors and fast-paced feature rollouts with tight cross-functional collaboration.”

Yusuf Qazi

Screened

Mid-Level Full-Stack Developer specializing in Flutter, React, and AWS

Edmonton, Alberta3y exp

“Frontend engineer with experience leading a Flutter cross-platform mobile app end-to-end, emphasizing layered architecture (UI vs service/business logic) and performance tuning (off-main-thread image processing via isolates). Also builds React + TypeScript dashboards with reusable components (TailwindCSS) and scalable state management (Context + reducers), and has handled urgent production issues with QA-verified rollouts.”

“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.”

“Frontend engineer focused on scalable React + TypeScript architecture and design-system style reusability, with demonstrated refactoring impact in legacy codebases. Delivered a high-visibility, filterable “Project Showcase” feature in 3 weeks (CMS-integrated, responsive, CI/unit-tested) and has implemented real-time UI updates via WebSockets for chat profile presence.”

“Early-career full-stack developer who has built and deployed learning projects including a school system dashboard with role-based access and a Node/Express message board, then applied that experience to a real restaurant analytics dashboard in development. Uses AI-assisted tools like Cursor to accelerate coding while manually reviewing outputs, and has hands-on experience debugging frontend/backend issues and validating API inputs.”

“Frontend/full-stack engineer who built a fast-food ordering web app and a minimal one-page real-time admin dashboard (Supabase + Postgres) with role-based access. Emphasizes scalable architecture, strict TypeScript validation, and quality practices (error boundaries, backend validation, unit testing), and has experience refactoring messy codebases into reusable components with consistent Tailwind theming.”

“Built FlowPilot, an AI-powered product that generates complete importable n8n workflows from natural-language prompts using a RAG pipeline (Qdrant + LangChain) and a multi-stage agent with a scoring/repair 'Judge' loop for intent alignment. Experienced in backend architecture across Laravel/Node microservices and production AI/RAG systems, plus performance debugging from async job offloading to database index tuning after ORM migrations.”

Andrew Fu

Screened

“Backend/infrastructure engineer in the EBS org focused on global server lifecycle and fleet reliability. Led a major modernization from manual, ticket-driven recovery to centralized Python services and operator tooling with DynamoDB-backed state, strong auth/allowlisting, and CloudWatch monitoring, plus an AWS Glue/S3/SNS data pipeline to join server and hardware datasets for global operational querying and automated recovery.”

Anjali Chepuri

Screened

“Built and deployed an LLM-powered financial document processing and summarization platform at Morgan Stanley using a production RAG pipeline (PDF ingestion, embedding-based retrieval, schema-constrained JSON outputs) delivered via FastAPI microservices on Kubernetes. Drove measurable impact (40% reduction in manual review time) and improved factual accuracy for numeric fields by 30% through metadata-aware retrieval, strict schemas, and post-generation validation, with a human feedback loop from financial analysts.”