Vetted AI Agents Professionals

“Recruiting/Talent Operations leader with deep Lever ATS and analytics experience who led a high-impact Grace Hopper Conference program, creating attribution tracking via unique links and coordinating IT/engineering/TA to drive thousands of applicants and improved operational outcomes. Redesigned recruiter–hiring manager intake and alignment workflows with automation, improving time-to-fill by ~20%, and led compliance initiatives including pay transparency and ADA-accessible job descriptions using CommonLook/PAC in partnership with Legal and Compensation.”

“Built a production AI agent for phishing fraud detection using n8n orchestration, Claude (Sonnet 4/MCP), VirusTotal, and JavaScript formatting to generate and deliver email-based reports via Gmail. Has experience evaluating detection accuracy against known examples, iterating via feedback, and presenting AI solutions to non-technical teams.”

Luis Mirantes

Screened

Executive engineering leader specializing in scaling ML platforms and FinTech systems

Chevy Chase, MD30y exp

“Founder of an AI-agent startup focused on high-speed train braking efficiency, with a subsystem already demoed to RENFE Spain and positive initial feedback. Has hands-on fundraising and investor presentation experience, and stands out for a highly analytical approach to venture building centered on ROI, competitive barriers, IP protection, and evidence-based decision making.”

“Data Analyst at U.S. Cellular who built production LLM solutions, including a Tableau-embedded chatbot that converts natural language questions into Oracle SQL and returns actionable KPI insights for non-technical users. Also authored MAD-CTI, a multi-agent LLM system for dark web hacker forum threat intelligence (published in IEEE Access) that outperformed single-agent approaches by 14%.”

“Applied LLMs and a graph-RAG architecture in Neo4j to automate an accounting firm's cross-checking of transactional books against tax regulations, indexing 1,000+ pages into a knowledge graph with vector search. Combines agentic LLM workflows with classical NER (Hugging Face/NLTK) and validates using expert-labeled held-out data plus precision/recall and measured accountant time savings after deployment.”