Vetted AI Agents Professionals

Gayatri Puwar Mahendroo

Screened ReferencesStrong rec.

Director-level Product Leader specializing in AI, platforms, and B2B SaaS

San Francisco, CA19y exp

“Senior product leader with 15+ years spanning FAANG-scale platforms and early-stage startups, from Skype’s bot framework at 300M MAU to AI-native enterprise products in fintech, banking, and data center operations. Particularly strong in human-centered AI, trust and transparency UX, and turning ambiguous 0-1 opportunities into shipped MVPs that win pilots, awards, and business traction—including a GenAI observability platform that earned a Google Accelerator award and later led to acquisition.”

“Product leader focused on human-centered AI, with standout experience leading agentic AI strategy for Microsoft’s Cloud Supply Chain during a major GPU demand surge after the OpenAI partnership. They combined platform thinking, embedded AI UX, and influence-based leadership to drive adoption, cut manual effort by 40%, reach 2,000 MAU, and help protect $50M in at-risk revenue, while also building an AI quiz coaching tool for middle-school academic Olympiad students.”

michael avrahamov

Screened

Executive engineering leader specializing in AI-first SaaS and cloud platforms

San Francisco Bay Area, CA22y exp

“Senior engineering executive who operated as an org-level CTO at both ServiceNow and Oracle, leading global teams up to 150 engineers and driving cloud-native, multi-tenant platform architecture. Particularly strong in observability, streaming systems, and security, with experience delivering large-scale systems ingesting data from millions of agents while building high-performing, high-DORA engineering organizations.”

“Early-stage founder building a service business targeting small clinics, already with one client. Identified the opportunity by helping a family member and then validating needs through direct client conversations; uses AI (including AI agents) for content generation and plans deeper workflow automation to scale cost-effectively.”

“Apple product leader with a rare mix of platform, commerce, UX, and AI workflow experience. They led the App Store Connect rebuild for 50,000+ developer partners, shipped high-impact pricing and payments improvements, and recently built Claude-powered investigation tooling that saves engineering time while preserving human judgment. Strong fit for human-centered AI roles where trust, measurable outcomes, and cross-functional execution matter.”

“McKinsey practitioner who built and deployed production LLM systems for consultants/clients, including a Power BI-integrated multi-agent chatbot (RAG + text-to-SQL + formatting) with custom Python orchestration, verification loops, and a 100+ case eval set achieving ~95% consistency. Also delivered a taxonomy-mapper agent that standardized inconsistent labeling for C-suite stakeholders, cutting a process from >2 weeks to <30 minutes through demos and business-focused communication.”

“Software engineering intern who built an internal AI-agent automation using the Gemini API to reduce manual CRM data entry, iterating prompts closely with analysts to address precision concerns. Also worked on a medical image-diagnostics LLM project involving fine-tuning and benchmarking multiple model approaches, and has quant/sales-trading experience building automated pricers for complex options and persuading sales teams to adopt them with ROI-focused metrics.”

“Fintech and AI product engineer who has owned major production rollouts, including Lending Club's banking-arm launch, and has since built LLM-powered decision systems for finance and climate use cases. Particularly strong in combining stakeholder management with pragmatic architecture choices like observability, deterministic pipeline design, RAG, and document-to-structured-data workflows.”

“Frontend engineer from Amazon Advertising who owned a sophisticated React/TypeScript ad creative builder used by advertisers and ad ops teams. Stands out for combining deep browser-level debugging with product-minded UX improvements that reduced support escalations and made complex multi-placement ad configuration faster and more reliable for power users.”