Vetted Generative AI Professionals

“Senior engineering executive and former VP of Architecture at Expedia who led a small, high-leverage architecture organization supporting an 8,000-person global engineering org. Brings a rare mix of executive people leadership and hands-on AI systems work, including OpenAI plugin integration, multi-agent booking architecture, and agentic fraud workflows that materially improved execution, automation, and customer-facing outcomes.”

Andrew Hoyt

Screened

Executive technology leader specializing in SaaS, cloud platforms, and enterprise engineering

Atlanta, GA14y exp

“Enterprise software CTO with rare breadth across deep architecture, large-scale org leadership, and AI transformation. He has led 250+ teams at Aderant and previously a 2000-person global engineering org at NCR, while still personally driving re-architecture, prototyping, debugging, and AI-native product innovation across legal, retail, and restaurant platforms.”

“Software Developer at Amazon Robotics who co-developed a congestion-aware path planning system optimizing robot routes across 23 warehouses. Built and operated a real-time, service-integrated pipeline using AWS (AppConfig, DynamoDB), Java, and Redis caching, and has hands-on experience debugging robot behavior on-site with rigorous testing and staged releases.”

“Built a production internal knowledge assistant using a RAG pipeline over large spreadsheets, PDFs, and support documents, using transformer embeddings stored in FAISS. Focused on real-world production challenges—format normalization, retrieval quality, hallucination reduction (context-only + citations), and latency—using hybrid retrieval, quantization, and containerized deployment, and communicated the workflow to non-technical stakeholders using simple analogies.”

“Architect/engineering leader who built a multi-tenant AI platform end-to-end, including a secure FastAPI orchestrator (JWT, RBAC, tenant isolation, auditing) and an extensible MCP tool-routing layer, then productionized it via fully containerized microservices (Docker, Postgres/pgvector, Redis). Also has strong governance and compliance experience (ARB with security/privacy/SOX) and has owned high-severity incidents through mitigation and RCA/RCCA, plus prior high-volume payments/accounting data pipeline design with audit-grade integrity checks.”

Jordan Lineberry

Screened

Senior Full-Stack Engineer specializing in Healthcare SaaS and supply chain systems

New York, NY11y exp

“Backend engineer with healthcare platform experience at Thirty Madison, combining Django for secure, data-heavy core services with FastAPI async microservices for real-time patient monitoring. Led Kubernetes migration with Istio service mesh, autoscaling (HPA), and stateful storage patterns, and implemented GitOps CI/CD using ArgoCD. Also built real-time Kafka streaming pipelines with reliability patterns like idempotent producers and offset management.”

Jay Desai

Screened

Executive Technology Architect specializing in FinTech, AI platforms, and cloud modernization

California, USA24y exp

“Entrepreneur building an early-stage community food-sharing, network-effects platform positioned as an alternative to restaurant-centric delivery models (e.g., DoorDash/Uber Eats) by enabling homeowners to act as micro-entrepreneurs. Has been piloting a local community program for nearly a year and uses experiments and customer feedback to validate hypotheses and assess scalability.”

“Aspiring founder building an AI governance and compliance startup for the finance industry, focused on agents that monitor data lakes/lakehouses to detect security vulnerabilities, PII exposure, and governance issues in real time. Has already formed an S-corp, has not raised capital yet, and approaches idea validation through Minimum Lovable Product testing with potential clients.”

“ML/LLM engineer with production experience building a multi-GPU LLM inference platform using TensorRT and vLLM, achieving ~40% p95 latency reduction through batching/KV caching, quantization, and CUDA/runtime tuning. Also has end-to-end orchestration experience (Kubernetes, Airflow) and has delivered real-time fraud detection systems at Accenture in close collaboration with non-technical risk and product stakeholders.”

Akhilesh Patil

Screened

Junior AI/ML Engineer specializing in FinTech and generative AI

Remote, USA2y exp

“Built an end-to-end AI bug triage dashboard that combined React/TypeScript, FastAPI, Postgres, and classical ML to reduce manual engineering triage work by about 40%. Stands out for pragmatic, product-minded AI engineering: choosing interpretable models when they were sufficient, designing human-in-the-loop UX for trust, and separately building an agentic RAG project with vector search, Neo4j knowledge graphs, and reranking.”

Jason Main

Screened

Executive product leader specializing in AI, SaaS, and Healthcare IT

Austin, TX20y exp

“Product leader with experience spanning AWS enterprise software commercialization, healthcare workforce platforms, and AI-enabled peer mentoring products. Notable wins include launching a healthcare internal resource pool product that offset declining revenue by 3x+ and helping commercialize Amazon call center software into a product that generated just under $25M in first-year revenue. Strong throughline is customer-led product strategy, cross-functional execution, and human-centered AI that augments rather than replaces people.”

“Built and owned an LLM support copilot at Stripe focused on improving agent ticket resolution. Designed the backend and ML system end to end, using RAG, Redis caching, hybrid vector search, and LoRA fine-tuning to achieve 40% lower latency and 22% higher response accuracy, with continuous quality monitoring via Ragas and related evaluation frameworks.”

“Built a production RAG-based "knowledge copilot" for support/ops using LangChain/LangGraph, implementing the full pipeline (ingestion, chunking, embeddings, vector DB retrieval/rerank, guarded generation with citations) and operating it as monitored microservices with CI/CD. Also designed an event-driven, streaming backend for real-time inventory ordering predictions that reduced stockouts by 25%, and has hands-on incident response experience stabilizing LLM API latency/5xx spikes using Datadog/APM and resilience patterns.”