Vetted Backend Engineers in the Greater Phoenix

Pre-screened and vetted in the Greater Phoenix.

WO

Staff AI Full-Stack Engineer specializing in LLMs, multi-agent systems, and Voice AI

Gilbert, AZ10y exp
DriveHealthStanford University
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Surya Teja - Mid-level Backend/Full-Stack Engineer specializing in AI and FinTech payments in Tempe, AZ

Surya Teja

Screened

Mid-level Backend/Full-Stack Engineer specializing in AI and FinTech payments

Tempe, AZ4y exp
StripeArizona State University

Full-stack engineer who has owned an operational reporting/dashboard product end-to-end—building a React UI, designing/implementing FastAPI services, and deploying/operating on AWS. Demonstrates strong performance engineering (Postgres query/index tuning using EXPLAIN ANALYZE) with concrete impact (reports reduced from tens of seconds to a few seconds) and a reliability mindset across observability, migrations, and resilient third-party/ETL integrations.

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Aakash Khepar - Mid-level Full-Stack AI Engineer specializing in agentic AI systems in Tempe, AZ

Aakash Khepar

Screened

Mid-level Full-Stack AI Engineer specializing in agentic AI systems

Tempe, AZ4y exp
Arizona State UniversityArizona State University

Full-stack engineer with strong ownership across production SaaS and AI agent systems, including a multi-tenant enterprise analytics product at Fractal Analytics and an archive intelligence platform for a real nonprofit. Stands out for combining deep backend/system design, secure AI/RAG implementation, and rapid zero-to-one execution—plus multiple hackathon wins and leadership roles.

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SS

Junior Full-Stack Engineer specializing in React, Node.js, and AWS

Phoenix, AZ2y exp
Nova AstroArizona State University
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SB

Entry-level Full-Stack Software Engineer specializing in AI/ML and cloud systems

Phoenix, AZ1y exp
BMR Pvt. LtdArizona State University

Software engineering intern who built and deployed a full-stack telemedicine platform (React/Node/MongoDB) used daily in a pediatric clinic, incorporating PyTorch-based predictive features. Demonstrated strong customer-facing iteration and production performance debugging—resolved a live slowdown by indexing/optimizing MongoDB queries and adding caching, improving response times by ~50%.

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