Applied AI Architect

Role overview This is a fractional, client-facing Applied AI Architect role focused on deploying an enterprise agentic integration and AI platform into complex customer environments. The architect leads executive-level engagements, owns technical rollout, and partners closely with product engineering to feed field realities back into the roadmap. It is an initial contract engagement with the possibility of extension or conversion to full time based on commercial outcomes.

Responsibilities - Lead executive discovery and scoping sessions with VP and C-level stakeholders, translating technical capability into business impact for CIO and CTO audiences. - Own end-to-end technical rollout into customer VPCs, on-prem Docker hosts, and firewalled networks, resolving blockers around SSO, enterprise OAuth, and egress policies. - Engineer data connectivity and context layers using Model Context Protocol and adjacent techniques so deployed AI agents are genuinely usable and trustworthy. - Identify adjacent business functions and high-value AI use cases to expand footprint beyond initial beachhead teams and prove ROI. - Represent the platform's agentic AI point of view in customer briefings and competitive situations, codifying field wins into repeatable go-to-market playbooks. - Partner with core engineering, which is building on the Claude Agent SDK, to bring field realities, edge cases, and customer needs back into the product roadmap.

Requirements - 5+ years of hands-on experience across Linux, Docker, networking, major cloud providers, and enterprise SSO/OAuth. - Hands-on experience building or deploying production systems powered by large language models or AI agents beyond weekend prototypes. - Strong executive presence, with the poise and clarity to handle live pushback from senior enterprise stakeholders. - Demonstrated ability to operate in zero-to-one, time-boxed engagements that prove a commercial business case and leave behind repeatable frameworks. - A hunter instinct for creating momentum without a preset playbook and comfort taking small, calculated bets to earn buy-in from scratch. - The instinct to mentor client teams, empower internal champions, and document scalable runbooks that outlive the engagement.

Nice to have - Deep judgment in distinguishing valuable enterprise AI use cases from AI theater, balancing technical reality with commercial tradeoffs. - Background representing a platform externally through writing, speaking, or open-source contributions.

Benefits and work setup - Initial fractional or project-based engagement with flexible scheduling built around client deployment milestones and on-site workshops. - Hourly compensation of USD $100 to $120, with potential to extend the contract or convert to a full-time role depending on market signals and business case validation.