Vetted Agile 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.”

Timothy Washington

Screened ReferencesStrong rec.

Senior Product Manager specializing in AI-powered products and responsible AI

Cranbury, NJ18y exp

“Mission-driven product and program leader who built WhatsApp’s talent inclusion infrastructure from scratch and later applied AI at Google to scale inclusive product evaluation across 25+ teams, including Gemini. Brings a rare mix of product systems thinking, human-centered AI judgment, and lived experience growing up in public housing that deeply informs their focus on equity, access, and accountable technology.”

“Backend/data engineer with deep healthcare data experience (FHIR, de-identification) across both GCP and AWS. Has built and operated production microservices and ETL pipelines (FastAPI, Dataflow, Glue) with strong reliability practices, and led modernization of a legacy SAS compliance reporting system to cloud services with validated parity and stakeholder-facing Looker comparisons.”

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.”

“Engineering leader/player-coach who built and validated a preference-based recommendation engine using clustering, including generating test data to evaluate how clusters evolve over time. Has SRE/DevOps experience and has owned production incidents end-to-end (logging-driven RCA and refactoring patterns that failed at large data scale). Emphasizes quality and platform stability via unit, integration, and load testing, and has managed performance via regular 1:1s and PIPs.”