Vetted Generative AI Professionals

“Early-stage investor with PropTech exposure who sources founders through conferences, VC networks, and thesis-driven outbound. Demonstrated ability to turn cold founder interactions into multi-month relationships by providing practical support on financial models and pitch decks, while applying a thoughtful lens on product-market fit and ICP in investment decisions.”

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.”

Jimmie Lee

Screened

Executive Technology & Strategic Operations Leader specializing in AI governance and cybersecurity

Greater Seattle Area, WA25y exp

“Serial entrepreneur who has launched 9+ bootstrapped businesses and has repeatedly raised capital as a CTO for other startups, leveraging a 7,000+ funder network and a sales-like fundraising pipeline approach. Deeply embedded in the VC/studio/accelerator ecosystem for 15+ years and currently mentors/advises with Stanford Venture Studios, Alchemist, and Ignyte in Dubai. Recently left Meta and is specifically targeting CTO roles for the next 5–10 years.”

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.”

Christopher Sipola

Screened

Senior Machine Learning Engineer specializing in LLMs and recommendation systems

New York, NY11y exp

“ML/GenAI engineer who owned major parts of Spotify’s AI DJ from offline experimentation through deployment, monitoring, and iteration. They combine recommender systems, RAG, real-time feedback loops, and LLM safety/orchestration to ship consumer-facing personalization features that drove double-digit engagement and deeper listening sessions.”

“Early-stage investor/sourcer with experience at Insight Partners and M13 who combines thesis-driven outbound with deep ecosystem presence to generate proprietary founder relationships. Particularly strong in generative AI, creative tooling, and commerce tech, with added credibility from Gold House Ventures fellowship, angel investing communities, and a fractional fundraising advisory practice that has helped founders close rounds.”

“Chief AI Strategy at startup Zenera with 30 years in technology strategy/design, experienced in building business plans across startups and large enterprises (projects, new products, and M&A). Deeply familiar with enterprise AI go-to-market challenges (slow procurement/pilot cycles) and preparing for VC expectations (e.g., targeting a16z) by emphasizing unique tech and paying customers.”

“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).”