Vetted MLOps 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.”

“Hands-on engineering leader currently at Vianai (GenAI enterprise applications) building AI-assisted data exploration/insights products with Python and React. Previously Corporate VP of Digital Health at Samsung Electronics, where they helped close key partner deals by demonstrating API-driven openness and led a major Samsung Health modernization from monolith to microservices for horizontal scalability and faster, safer iteration.”

“Former Bain strategy consultant with ~3 years experience and an MBA, with examples spanning turnaround work, consumer goods market assessment, and strategic planning. Demonstrates strong executive alignment habits (disciplined updates, no-surprises communication) and operational leadership, including training teammates on Tableau via templates to scale productivity.”

Jesse Olson

Screened

Executive AI technology leader specializing in agentic AI and enterprise transformation

22y exp

“Candidate is exploring a startup in the autonomous agentic AI space and is approaching it with a disciplined founder mindset. They are evaluating market opportunity, competition, customer pain, ROI, technical feasibility, and MVP options while already speaking with potential customers across verticals to validate needs and buying processes.”

“Senior engineering/technology leader with experience driving board-backed, multi-year platform transformations in retail commerce (American Eagle) and scaling ML/product delivery platforms at Amazon Alexa. Known for data-driven build-vs-buy decisions, org scaling (6→80), and measurable outcomes including 98.5%→99.9% uptime, 234 experiments generating ~$200M revenue lift, and increasing delivery velocity from 1.5 to 30 features/week while transitioning to DNN/large-model architectures.”

Somya Upadhyay

Screened

Senior Software Engineer specializing in cloud infrastructure and large-scale data pipelines

Bellevue, USA9y exp

“Backend engineer on Amazon’s Geospatial Data team (Amazon Maps) who built a real-time road-layer service ingesting third-party and internal signals to deliver road closures/traffic overlays to delivery drivers on a ~3-minute cadence while minimizing mobile data egress. Demonstrates strong production reliability skills (rate limiting, idempotency, cache stampede prevention) and security depth (IAM, RBAC, tenant row-level security), plus careful handling of edge cases like manual override protection against automated feed overwrites.”

“Built and deployed a production LLM conversational AI system at OpenAI supporting chat, summarization, and semantic search at 1M+ requests/day, driving major latency (40%) and accuracy (25%) improvements through Pinecone optimization and tighter RAG with re-ranking. Also has Amazon experience improving recommendation systems by translating ML metrics into business terms to boost CTR and conversions, with strong MLOps/orchestration depth (Airflow, MLflow, SageMaker, Kubeflow).”

“ML engineer with 5 years at Stripe building and productionizing real-time fraud detection at massive scale (3M+ transactions/day; $5B+ annual payment volume). Delivered measurable impact (22% accuracy lift, 18% loss reduction, +3–5% authorization rates) and has strong MLOps/orchestration experience (Docker, Kubernetes, Airflow, MLflow, CI/CD, monitoring/rollback) plus a structured approach to LLM agent/RAG evaluation.”

“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.”

“Candidate brings experience across both startups and big tech, along with direct exposure to pitching ideas to several VCs. They show a practical founder mindset centered on rapid prototyping, customer feedback, retention, and building AI/agent-driven products with strong product-market fit potential.”