Vetted Data Ingestion 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.”

Chase Bradley

Screened ReferencesModerate rec.

Staff AI Engineer specializing in LLM systems, retrieval, and ML infrastructure

Remote10y exp

“ML/LLM engineer from Cohere who has owned retrieval, reranking, agentic workflows, and internal evaluation infrastructure end-to-end in production. Particularly strong in turning brittle RAG and research-heavy ideas into scalable enterprise systems with grounded outputs, lower customer escalations, and adoption by major clients like Notion and Fujitsu.”

“Built a zero-to-one, LLM-powered compliance automation platform for AWS Marketplace that reduced reliance on manual review for large-scale product publishing workflows. Stands out for combining full-stack engineering, internal platform integrations, and production AI evaluation in a highly regulated, operationally complex environment.”

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.”

“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.”