Chris M.

𝗠𝗼𝘀𝘁 𝗔𝗜 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗱𝗶𝗲 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗣𝗢𝗖 𝗮𝗻𝗱 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻. 𝗜 𝘀𝗵𝗶𝗽 𝘁𝗵𝗲 𝗼𝗻𝗲𝘀 𝘁𝗵𝗮𝘁 𝗱𝗼𝗻'𝘁. Companies say they want one person who can set AI strategy, be AI-native enough to build it, lead the engineers who scale it, and deliver product. 𝗧𝗵𝗮𝘁 𝗽𝗲𝗿𝘀𝗼𝗻 𝗶𝘀 𝗺𝗲. I'm a 𝗙𝗿𝗮𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗖𝗧𝗢 / 𝗖𝗵𝗶𝗲𝗳 𝗔𝗜 𝗢𝗳𝗳𝗶𝗰𝗲𝗿 𝗮𝗻𝗱 𝗵𝗮𝗻𝗱𝘀-𝗼𝗻 𝗯𝘂𝗶𝗹𝗱𝗲𝗿. I lead AI product strategy and architecture, then get into the code: AI agents, RAG pipelines, and automation, plus the observability, guardrails, and governance that keep them alive in production. I separate deterministic infrastructure from probabilistic reasoning so failures are traceable and costs are bounded. 𝗜 𝗯𝘂𝗶𝗹𝗱 𝘄𝗶𝘁𝗵 Cursor, Claude (incl. Claude Code and MCP), OpenAI, Python, and a modern full stack: React / Next.js / Node / TypeScript, Supabase / PostgreSQL, on AWS. I orchestrate workflows with n8n, Make, and Zapier, and ship production voice and agent systems (Retell, Vapi, Twilio) for regulated environments. 𝗔𝗻𝗱 𝗜 𝗹𝗲𝗮𝗱. I set the roadmap, make the build-vs-buy and model calls, stand up evaluation and governance, and bring junior engineers up to deliver. You get a strategist who has actually built the thing, not a deck, and not a coder who's never owned production. 𝗥𝗲𝗰𝗲𝗻𝘁 𝗔𝗜 𝗯𝘂𝗶𝗹𝗱𝘀: - 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗥𝗔𝗚 𝘄𝗶𝘁𝗵 𝗮𝘁𝘁𝗿𝗶𝗯𝘂𝘁𝗲-𝗯𝗮𝘀𝗲𝗱 𝗮𝗰𝗰𝗲𝘀𝘀 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 (𝗔𝗕𝗔𝗖): granular, per-user control over what data a chatbot can surface, so RAG is safe in regulated environments. - 𝗦𝗲𝗿𝘃𝗲𝗿𝗹𝗲𝘀𝘀 𝗥𝗔𝗚 𝗼𝗻 𝗔𝗪𝗦 that costs $0 at rest and scales cost with revenue, ingesting email, documents, and file storage. - 𝗔𝗜 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗮𝗻𝗱 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗽𝗼𝗿𝘁𝗮𝗹 𝗳𝗼𝗿 𝗮 𝗹𝗮𝘄 𝗳𝗶𝗿𝗺: automated intake, classification, and review across long, multi-source documents. - 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝘁𝗵𝗮𝘁 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗮 𝗵𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲 𝗿𝗲𝗳𝗲𝗿𝗿𝗮𝗹 𝘀𝘆𝘀𝘁𝗲𝗺: POC to production in 8 weeks, 60% less manual review. - 𝗪𝗵𝗶𝘀𝗽𝗲𝗿 𝗺𝗲𝗲𝘁𝗶𝗻𝗴-𝘁𝗼-𝘁𝗿𝗮𝗻𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝗼𝗻 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 that replaced a vendor and saved $3K/month. - 𝗥𝗲𝗮𝗹-𝘁𝗶𝗺𝗲, 𝗥𝗲𝗱𝗶𝘀-𝗯𝗮𝘀𝗲𝗱 𝗶𝗻-𝗰𝗮𝗹𝗹 𝘀𝗮𝗹𝗲𝘀 𝗰𝗼𝗮𝗰𝗵𝗶𝗻𝗴 𝗮𝗽𝗽 for live conversations. 𝗥𝗲𝗰𝗲𝗻𝘁 𝗹𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗿𝗼𝗹𝗲𝘀: - 𝗔𝗜 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗿, 𝗦𝘁𝗿𝗶𝗱𝗲 (𝗹𝗮𝗿𝗴𝗲 𝗨𝗦 𝗼𝗻𝗹𝗶𝗻𝗲 𝗲𝗱𝘂𝗰𝗮𝘁𝗶𝗼𝗻 𝗽𝗿𝗼𝘃𝗶𝗱𝗲𝗿): own the AI lifecycle and governance for the tooling that QAs course content at scale, evals, guardrails, and human-in-the-loop QA. - 𝗟𝗲𝗱 𝘁𝗵𝗲 𝗼𝗿𝗶𝗴𝗶𝗻𝗮𝗹 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝘁𝗲𝗮𝗺 𝗮𝘁 𝗣𝗹𝗮𝗶𝗱 that built its first data automation platform. - 𝗟𝗲𝗱 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝘁𝗲𝗮𝗺𝘀 𝗮𝘁 𝗠𝗫: financial data automation. - 𝗟𝗲𝗮𝗱 𝗔𝗜 𝗮𝗻𝗱 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝗻𝘀𝘂𝗹𝘁𝗮𝗻𝘁, 𝗨𝗗𝗥 (𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗹𝗮𝗿𝗴𝗲𝘀𝘁 𝗨𝗦 𝗿𝗲𝘀𝗶𝗱𝗲𝗻𝘁𝗶𝗮𝗹 𝗥𝗘𝗜𝗧𝘀): 3-year engagement leading a team that built AI and automation across the company, from tenant background checks to onboarding to AI alarm monitoring. - 𝗔𝗜 𝗥&𝗗 𝗟𝗲𝗮𝗱, 𝗣𝗲𝗿𝗹𝗺𝗮𝗻 𝗠𝗲𝗱𝗶𝗰𝗮𝗹 𝗚𝗿𝗼𝘂𝗽 (𝟳𝟱𝟬-𝗽𝗲𝗿𝘀𝗼𝗻 𝗦𝗮𝗻 𝗗𝗶𝗲𝗴𝗼 𝗺𝗲𝗱𝗶𝗰𝗮𝗹 𝗴𝗿𝗼𝘂𝗽): stood up their AI R&D pod and led and upskilled their junior engineers, building healthcare AI like referral automation. - 𝗛𝗲𝗮𝗱 𝗼𝗳 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗮𝗻𝗱 𝗣𝗿𝗼𝗱𝘂𝗰𝘁, 𝗩𝗲𝗿𝘁 𝗘𝗻𝗲𝗿𝗴𝘆 𝗚𝗿𝗼𝘂𝗽: LL97.ai building-emissions compliance SaaS - 𝗙𝗿𝗮𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗖𝗵𝗶𝗲𝗳 𝗔𝗜 𝗢𝗳𝗳𝗶𝗰𝗲𝗿, 𝗗𝗲𝗹𝗹𝗶𝗻𝗶 (𝗔𝗜 𝗳𝗶𝗿𝗺 𝗳𝗼𝗿 𝗺𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴): led development of new AI practice - 𝗙𝗿𝗮𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗖𝗧𝗢, 𝗥𝗲𝗻𝗱𝗲𝗿 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀: develop AI practice for an 25-year-old analytics agency - 𝗙𝗿𝗮𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗔𝗜 𝗢𝗳𝗳𝗶𝗰𝗲𝗿, 𝗔𝗥𝗘 𝗚𝗹𝗼𝗯𝗮𝗹: owned strategy, architecture, and a build-ready blueprint for a high touch coaching and consulting firm I work best with companies that already understand 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗔𝗜 𝗶𝘀 𝗮 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗴𝗮𝗺𝗲 𝘁𝗵𝗮𝗻 𝗮 𝗱𝗲𝗺𝗼 and have decided production-grade AI is what their organization needs. The seriousness matters more than the sector. Regulated, ops-heavy, data-intensive environments are the archetype, 𝗵𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲 (𝗛𝗜𝗣𝗔𝗔), 𝗳𝗶𝗻𝘁𝗲𝗰𝗵, 𝗹𝗲𝗴𝗮𝗹, 𝗹𝗼𝗴𝗶𝘀𝘁𝗶𝗰𝘀, but I've shipped just as well across 𝗲𝗻𝗲𝗿𝗴𝘆, 𝗿𝗲𝗮𝗹 𝗲𝘀𝘁𝗮𝘁𝗲, 𝗰𝗼𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻, 𝗲𝗱𝘂𝗰𝗮𝘁𝗶𝗼𝗻, 𝗲-𝗰𝗼𝗺𝗺𝗲𝗿𝗰𝗲, 𝗮𝗻𝗱 𝗦𝗮𝗮𝗦 𝘀𝘁𝗮𝗿𝘁𝘂𝗽𝘀. If your AI has to hold up under real data, real users, and real consequences, 𝘁𝗵𝗮𝘁'𝘀 𝗺𝘆 𝗹𝗮𝗻𝗲. 𝗧𝗿𝗮𝗰𝗸 𝗿𝗲𝗰𝗼𝗿𝗱: 15+ years shipping production systems. MBA. AWS Solutions Architect. Led teams and developed automation platforms. Cut inference costs 𝟰𝟬% by restructuring agent architecture. Prevented a six-figure architectural misstep in fintech. Trained 200+ professionals. I run an applied AI reliability lab, so client work is backed by ongoing research.