Vetted AI & Machine Learning Professionals in the Bay Area

Jonathan McGrath

Screened ReferencesModerate rec.

Senior Full-Stack/AI Engineer specializing in mobile and web product development

San Francisco, CA14y exp

“Built an end-to-end mobile + web Q&A marketplace connecting users with professionals, including real-time chat and Stripe-based monetization (products/subscriptions). Hands-on with scaling Firebase/Firestore (subcollections, composite indexing, pagination) and mobile caching/sync challenges. Also created an internal AI-driven report generator that turned chatbot outputs into curated graphs and PDFs for marketing, iterating based on stakeholder feedback.”

“Healthcare-focused ML/AI engineer who has built production voice agents and clinical question-answering systems end-to-end, from experimentation through deployment, observability, and iteration. Particularly strong in making LLM systems reliable in real workflows via RAG, fine-tuning, guardrails, evaluation pipelines, and shared Python tooling; cites ~20% clinical QA accuracy gains and ~40% faster physician decision turnaround.”

“LLM/agentic systems builder who has deployed production solutions for a resource management firm, using an MCP-driven architecture with Neo4j + Elasticsearch and a ChatGPT frontend to generate candidate/company “SmartPacks” and answer entity Q&A. Also built a LangGraph/LangSmith-orchestrated multi-agent workflow that automates data-infra change requests end-to-end (impact analysis, SQL + tests, and PR creation), and delivered a ~60% latency reduction through TTL-based context caching while improving accuracy via a business data dictionary.”

“Senior AI/ML engineer with hands-on experience building production LLM systems in healthcare, including RAG-based clinical question answering and end-to-end MLOps on Vertex AI and Kubernetes. They combine strong platform engineering with applied GenAI work, citing a 35% improvement in factual accuracy and a 30% boost in internal team productivity through modular Python services and CI/CD.”

“AI/ML engineer with hands-on experience building a healthcare-focused generative AI application end-to-end, from architecture and data design through deployment, monitoring, and iterative improvement. Particularly strong in multi-agent LLM systems, fine-tuning, and safety guardrails, with measurable impact including a 20% accuracy lift to 91% and 10% latency improvement in a nutrition recommendation pipeline.”

“At ReferU.AI, designed and deployed an agentic RAG pipeline that automates multi-jurisdiction legal document drafting, emphasizing hallucination reduction through hybrid retrieval, validation agents, guardrails, and iterative regeneration. Experienced with orchestration frameworks (especially CrewAI) and rigorous testing/evaluation practices including human-in-the-loop review, adversarial testing, and production metrics/logging.”

“Cloud/distributed-systems engineer who has shipped real-time, offline-capable ledger/expense infrastructure and solved tricky cross-layer production bugs (carrier handoff retries causing duplicate writes) using packet captures and device logs. Also built modular Python ETL/catalog pipelines for e-commerce with config-toggled plugins for customer-specific pricing/SKU rules, and iterated product changes directly with on-site fulfillment operators using feature flags.”

Tao Chen

Screened

Senior Full-Stack Engineer specializing in AI-powered web products

Mountain View, CA7y exp

“Backend/data engineer who has built production AI video generation services on AWS using a hybrid serverless + container architecture (FastAPI, Lambda, ECS, Postgres/Redis) with strong reliability practices (auth, retries/timeouts, structured logging, CloudWatch + Slack alerting). Also delivered AWS Glue ETL pipelines with schema evolution handling and modernized a legacy SAS healthcare reporting workflow to Python with parity validation and parallel-run migration.”