Vetted Generative AI Professionals

“Engineering leader who built and delivered an IoT smart-spaces platform for the self-storage and smart-living domains, translating customer requirements into architecture, capability maps, and a multi-milestone roadmap. Personally stood up missing AI/ML capabilities (including churn prediction) using Databricks (Delta Lake/MLflow), enabling follow-on features like energy optimization and security/anomaly detection. Scaled an org from 20 to 80+ with disciplined Agile planning (Jira Advanced Roadmaps/Confluence) and strong executive/customer-facing leadership during high-stakes customer commitments.”

“ML/NLP leader with 12+ years of impact across LinkedIn, TikTok, and Levi's, building and productionizing multimodal recommendation and embedding-based search systems. Deep experience in entity resolution, vector retrieval, and rigorous evaluation, with cloud-native deployment/monitoring (MLflow, Airflow, SageMaker/Lambda, Azure ML, Kubernetes) and demonstrated double-digit relevance gains at millions-of-users scale.”

“Founder of a self-funded side advisory (e-Regency Advisory Services LLC) focused on helping early- and growth-stage technology companies improve customer experience, modernize cloud platforms, and build customer success/operations strategies leveraging AI. Has worked closely with founders and startup leadership teams and has supported companies as they prepared for or pursued funding, bringing a pragmatic, operationally disciplined approach to validating and scaling new ideas.”

“Automotive technology leader who has driven ADAS/autonomous driving (L2++ to L4) and AI-assistance roadmaps for Mercedes and Volkswagen Group. In current role, built a software-defined vehicle organization from a handful of people to ~150 supporting VW, Porsche, and Audi, while setting scalable architecture foundations across brands and vehicle classes.”

“AI/LLM engineer with production experience at NVIDIA and Microsoft, including building a RAG-based enterprise knowledge assistant that improved accuracy by 42% and scaled to thousands of queries. Deep in inference optimization (TensorRT-LLM, Triton, quantization, speculative decoding) and MLOps/observability (Prometheus/Grafana, MLflow, LangSmith), plus orchestration with Kubeflow/Airflow across multi-cloud.”

“Amazon internship experience building production GenAI analytics for the returns organization: a multi-agent LLM+RAG system that let analysts query multiple heterogeneous data sources in natural language without hand-written SQL. Also built and operationalized four Apache Airflow DAGs for large-scale ETL, emphasizing observability and freshness-aware metadata to keep outputs accurate and up to date.”

“Engineering/product leader with repeated experience defining roadmaps and delivering platform migrations across SaaS and enterprise contexts. Scaled an education system from two districts to 2M+ students statewide, and led an Elasticsearch-based architecture for an internal Disney product serving ~100K employees. Known for rigorous execution (RAID/OKRs/sprints), strong stakeholder communication (weekly AI summaries), and empowering teams via end-to-end ownership and customer-connected development.”

“Operator focused on scaling and growing organizations, with experience in PE/VC-backed environments. Familiar with PE/VC and accelerator/studio models and how to apply them to create business agility and rapid experimentation while preserving innovation as companies scale.”