Vetted Artificial Intelligence Professionals

“Frontend engineer focused on scalable architecture and long-term maintainability, emphasizing modular component design in React + TypeScript. Has experience building reusable component libraries (including Storybook and shared repos) and collaborating with design/product to refine UX workflows, reducing development time over successive iterations.”

“Recruiting leader who has managed teams of up to 6 while remaining hands-on with requisitions, owning end-to-end process design, ATS administration, and performance metrics reporting to senior leadership (including CFO/CPO). Built retention and quality feedback loops (7/30/90-day, 1-year check-ins and exit interviews) and drove process improvements like job description revisions to increase candidate quality and secure additional headcount when demand exceeded capacity.”

“Technical founder/engineering leader who has repeatedly built organizations and platforms from zero to scale, including co-founding Direct Derm and creating a full stack system now handling hundreds of thousands of dermatology consultations. Experienced in MVP-led execution to unlock funding, scaling teams with a mix of in-house and offshore talent, and leading API-first/serverless architecture with release-based security scanning and annual pen testing.”

“ML/NLP engineer with Industrial IoT experience who built an end-to-end anomaly detection and GenAI explanation system: AWS (S3, PySpark, EC2/Lambda) pipelines feeding dashboards, plus transformer-embedding vector search to connect anomalies to noisy maintenance notes and past events. Demonstrated measurable impact (15% lift in defect detection; ~35% reduction in manual review; 35% fewer preprocessing errors) and strong productionization practices (orchestration, monitoring, rollback, data-quality controls).”

“CTO-level leader who drove a major post-buyout transformation at NPact—modernizing engineering (CI/CD, QA, observability), moving products toward SaaS/cloud, and scaling the org from ~20 to ~70 while maintaining 97% retention. Uses instrumentation and workflow analytics (including Atlassian-derived data) to improve delivery, citing an ~80% reduction in feature/bug churn through better scoping and requirements. Comfortable with board-level ROI decisions and customer/fundraising conversations, translating technical tradeoffs into clear business outcomes.”

“Backend/LLM engineer who ships production internal tooling end-to-end: automated data-request processing with monitoring-driven improvements (better error diagnostics and lower latency via query/index tuning). Also built a RAG-based internal Q&A system over company docs and operational logs with guardrails (similarity thresholds, fallbacks, response limits) and an eval loop using real user queries and human review to drive prompt/retrieval changes.”

“AI/LLM practitioner who has deployed production chatbots across e-commerce, HRMS, and real estate, focusing on retrieval-first workflows for factual tasks like product and property search. Optimized intent understanding and significantly improved latency by using lightweight embeddings and tuning the inference pipeline on Groq (Llama 3.3), while applying modular orchestration and measurable production evaluation.”