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Vetted Terraform Professionals

Pre-screened and vetted.

MM

Entry Backend Software Engineer specializing in Python/FastAPI and cloud-native APIs

Milpitas, CA0y exp
California State University, ChicoCalifornia State University, Chico

Backend engineer who built and evolved a low-latency document search platform (C++/gRPC on Kubernetes with a vector database), emphasizing resilience under concurrent load through strict deadlines, retries, idempotency, and observability. Also experienced building secure, frontend-friendly FastAPI services (Pydantic + JWT) and executing safe incremental refactors using feature flags and parallel validation.

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MH

Minh Huynh

Screened

Junior AI/ML Engineer specializing in LLM systems and personalization

Anaheim, California2y exp
Reach BrandsCity University of Seattle

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.

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SN

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

Calgary, Canada1y exp
Schulich Ignite, University of CalgaryUniversity of Calgary

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.

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MF

Mid-Level Software Engineer specializing in cloud data platforms and CI/CD

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.

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