Vetted Fraud Detection Professionals

“Full-stack/backend engineer with recent hands-on experience building an AI-powered AML/KYC platform for financial institutions, spanning Go/Python backend services, real-time risk pipelines, and React/TypeScript analyst dashboards. Stands out for measurable compliance-tech impact: 94% fewer false positives, review times reduced from days to hours, and microservices processing 10,000+ transactions per second.”

Christopher McLean

Screened

Executive engineering leader specializing in security, privacy, and high-scale infrastructure

Brookline, MA18y exp

“Veteran operator from top-tier tech companies including PayPal, LinkedIn, Stripe, Facebook, and Merge, now seeking the right startup idea to found. Brings hands-on angel investing experience across 5 startups in the EU and US plus additional exposure through X-Stripe Angels, giving them both operator and investor perspective.”

“Backend/ML platform engineer with Google experience leading Python microservices for an AI-driven recommendation/retrieval system, including PyTorch inference and a retrieval-augmented generation workflow. Strong in production Kubernetes + GitOps (ArgoCD), real-time Kafka/Spark pipelines, and phased on-prem/legacy to AWS/GCP cloud migrations with reliability-focused rollout and rollback practices.”

Sean Brennan

Screened

Senior Software Engineer specializing in backend platforms and cloud systems

Oakland, CA28y exp

“Engineer with a track record of taking on messy, high-ambiguity systems that others avoid, including a Python rewrite of Google Search Appliance's settings migrator that achieved 95% test coverage and zero bugs. Has also built internal full-stack planning tools at Capital One using React, Kotlin, and Airflow/DBT data pipelines, and brings specialized insurance analytics experience from catastrophe risk modeling for insurers.”

“Product leader at Mastercard who has repeatedly built and commercialized AI-driven fraud products across payments and healthcare, including a patented ML approach and a fraud suite that won 30+ enterprise customers in its first year. Stands out for combining deep ML product judgment with workflow design, explainability, and human-in-the-loop thinking to drive real operational change.”

“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.”

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.”