Vetted Software Development Engineers

Bocheng Wan

Screened

Mid-level Software Engineer specializing in consumer robotics and machine learning

Sunnyvale, CA4y exp

“Developer with a structured AI-assisted coding workflow centered on algorithmic rigor, type-safe scaffolding, and spec-driven modular design. Used Claude Code to help build an Android SDK for IPC, quickly ramping up on AIDL and compressing delivery including unit tests from several weeks to two weeks.”

“AI-native engineer who says 100% of current development is done through coding agents and reports roughly 10x productivity gains. Currently building 'AI TV,' a production video-generation system with multi-agent orchestration, including a motion-graphics pipeline where agents handle high-level design, low-level implementation, and iterative review. Particularly strong in designing codebases and workflows so AI systems can effectively use architectural and product context.”

“Backend-focused engineer with Walmart Global Tech experience building shipment and seller workflow systems using Spring Boot, GraphQL, Kafka, and async processing. Stands out for improving bulk label API performance by 60-75%, designing item-level partial-failure workflows that improved user clarity, and also exploring AI-powered debugging/RCA platforms with Java, Python, LangChain, and LLM integrations.”

“Game UI engineer with shipped experience on Paladins (Unreal Engine 3/C++), including an in-game store with automated grid-based recommendation layout and an inventory UI built for PC/PlayStation/Xbox/Switch. Emphasizes production readiness through clean separation of UI/data, strong documentation for engineering and content workflows, and memory-conscious UI patterns (pooled entries/paged navigation) for constrained platforms like Switch.”

“Built and operated a production warehouse metadata collection platform at Sigma Computing, integrating Go/gRPC services with a TypeScript backend and MySQL, with strong emphasis on idempotency, retries, bounded-concurrency job queues, and Datadog-based observability. Also created Kurral (kurral.com), an AI agent runtime security and observability/governance SDK/proxy concept, iterating via pilot-customer feedback and market research; targeting founding engineer roles with $180–200k base and ~2–5% equity.”

“Backend/AI engineer who has built a rule-service platform on AWS and evolved it into an agentic RAG system using LangChain, ReAct, tool calling, and LLM-as-judge review. Notable for combining heavy AI-assisted development with production safeguards like manual CR, CloudWatch monitoring, fallback strategies, benchmark testing, and user-feedback-driven model improvement.”

Arun Ganesh

Screened

Junior Software Engineer specializing in healthcare AI and cloud infrastructure

San Francisco, CA3y exp

“Amazon Health AI engineer who has owned both full-stack clinical product features and production LLM systems end to end. Built HIPAA-compliant GraphQL and agentic RAG architectures for provider workflows across 125,000+ patients, with measurable impact including 30% higher clinical relevance, 55% lower lookup time, and 12% less false medical information.”

“Backend/full-stack engineer (Amazon experience) who built an AWS-based integration testing platform using Flask, ECS, Docker, and CloudWatch—cutting 1000+ test cases from ~5 hours to ~30 minutes while improving log visibility for non-engineering users. Also led a zero-downtime EU region migration with rigorous ORR testing, and built a Kinesis/Firehose/S3 + Glue/Spark replay mechanism for resilient data recovery. Side project: reproducible, cost-efficient LLM hosting platform on EKS using CDK and Karpenter for scale-to-zero.”

Kunj Amrutbhai Patel

Screened

Mid-level Software Engineer specializing in distributed backend systems on AWS

Seattle, WA4y exp

“Built production systems in the AWS ecosystem, including an internal AI assistant for diagnosing account transfer and permissions issues and an end-to-end account transfer workflow used by enterprise customers. Stands out for combining LLM/RAG design with strong distributed systems reliability practices, emphasizing guardrails, fallbacks, and operational trust in high-stakes workflows.”

“Full-stack engineer with Uber experience building finance/billing reconciliation systems: shipped and owned an internal operations dashboard (Next.js App Router/TypeScript) that cut investigation time from hours to minutes and improved load time from ~6–7s to <2s. Deep in Postgres modeling and performance (sub-200ms optimized queries) plus durable event-driven workflow orchestration with idempotency, retries/backoff, DLQs, and reconciliation jobs; also has seed-to-Series C startup experience emphasizing end-to-end ownership.”