Vetted Infrastructure as Code Professionals

Stephen Koo

Screened

Senior Software Engineer specializing in LLM infrastructure and AI inference platforms

New York, NY8y exp

“Google Workspace AI engineer who owned major AI assistant infrastructure end-to-end: React/TypeScript UI, a Node.js context-aggregation gateway on Cloud Run, and a Go inference layer on GKE serving Gemini. Built and productionized Gmail RAG + agentic workflows with rigorous evals and guardrails, and has a strong track record of measurable impact (latency, engagement, acceptance-rate lifts) and zero-incident migrations using feature flags/strangler patterns across multiple ML teams.”

“Backend/data engineer with experience on large-scale consumer platforms (Google and Meta), building high-traffic Python microservices (REST/gRPC) on Kubernetes with strong reliability/observability practices. Delivered AWS container-based deployments with CI/CD and IaC, and built AWS Glue ETL pipelines on S3 with schema evolution and data quality controls; also has demonstrated SQL tuning impact (15% latency reduction) and incident ownership for batch pipelines.”

Kaiqi Zhang

Screened

Senior Full-Stack Engineer specializing in cloud-native SaaS and AI applications

Bellevue, WA13y exp

“ML/LLM engineer with hands-on experience shipping production RAG systems at Google Clinical Search and GenAI recommendation/summarization features in Meta Ads. Stands out for combining research-to-production execution with rigorous grounding, evaluation, safety checks, and reusable Python platform components that improved both reliability and team velocity.”

Jacob Zimmerman

Screened

Director-level Engineering Platform leader specializing in cloud infrastructure, SRE, and SaaS compute

New York, NY19y exp

“Longtime early-stage startup operator (since 1998) now running a solo consulting practice focused on SaaS infrastructure and platform strategy. Former angel investor in a medical information SaaS company that went public, with a strong focus on making prototype/demo systems enterprise-ready through efficiency, reliability, and performance improvements, and a nuanced view of how AI/LLMs may change engineering team models.”

Kevin Wang

Screened

Senior Software Engineer specializing in cloud infrastructure and distributed systems

Adams, MA8y exp

“Amazon engineer focused on productionizing LLM-powered developer workflows, including code assistance, debugging automation, and internal AI tooling. Stands out for combining hands-on ML systems work with strong platform engineering, including an orchestration engine that reportedly saved about $10K/day and reduced a manual workflow from 12 hours to under a second.”

Muneendra M

Screened

Mid-level Software Engineer specializing in event-driven backend and AI-enabled systems

San Francisco, CA4y exp

“Full-stack engineer at Stripe who owned a webhook monitoring and retry platform end-to-end, spanning backend services, React dashboards, and production operations. Stands out for combining strong distributed-systems judgment with product polish, including a reported 31% improvement in webhook delivery reliability and UI improvements that reduced support burden.”

“IBM Power/AIX infrastructure engineer who has owned large-scale Power9/AIX 7.x estates across primary/DR (VIOS/HMC/vHMC) and has deep hands-on experience with DLPAR, shared processor pool governance, and PowerHA recovery. Also brings modern DevOps/IaC capability—built Azure DevOps/GitHub CI/CD for Terraform + Kubernetes/Helm with strong security controls and resolved a production Terraform state-lock failure by redesigning backend locking and pipeline concurrency.”