Enterprise AI Solutions Architect

We're looking for a hands-on Enterprise AI Solutions Architect to own the technical direction of a broad, AI-powered enterprise collaboration ecosystem. You'll be responsible for designing, building, integrating, running, and continuously refining production AI agents, reusable skills, workflows, search functionality, and platform integrations across the enterprise. This is an individual-contributor role with no direct reports, but it carries real leadership weight — you'll set technical standards, mentor fellow engineers and administrators, review proposed solutions, and act as the go-to expert when things get complicated. The right candidate pairs deep, applied AI engineering skill with hands-on enterprise systems administration experience. Enterprise background is strongly preferred. Core Responsibilities - Build, test, ship, and maintain production-grade AI agents, skills, prompts, tools, workflows, and automations. - Develop AI capabilities using Model Context Protocol (MCP), function calling, APIs, and other agent-framework and integration technologies. - Turn business needs into technical designs, prototypes, and dependable, production-ready AI features. - Create secure integrations spanning collaboration tools, productivity suites, search platforms, AI assistants, and other enterprise systems. - Build APIs, connectors, webhooks, search integrations, retrieval-augmented generation (RAG) pipelines, and semantic search capabilities. - Make sure AI systems honor source-system permissions, access controls, data security rules, and governance requirements. - Design and assess approaches to grounding, citations, memory, retrieval, and context management within AI applications. - Evaluate AI solutions for quality, reliability, safety, performance, grounding accuracy, and production readiness. - Manage and tune enterprise AI platforms — covering access provisioning, configuration, integrations, monitoring, and lifecycle upkeep. - Diagnose and resolve complex problems involving AI platforms, integrations, connectors, identity systems, and data retrieval. - Build out reusable technical standards and patterns covering AI agents, integrations, configuration, testing, deployment, and security. - Run technical reviews and offer hands-on guidance and mentorship to other engineers and administrators. - Act as the senior escalation point for tricky AI platform and integration problems. - Track emerging AI capabilities and advise platform leadership on where to invest. Must-Haves - Track record of building and maintaining production-quality AI agents, skills, assistants, workflows, or integrations. - Solid grasp of AI application fundamentals: agents and agentic workflows, skills and tools, function calling, Model Context Protocol, prompt and context design, retrieval-augmented generation, enterprise search, and data connectors. - Strong understanding of grounding, citations, memory, context management, model/agent evaluation, AI safety, and access controls. - Demonstrated ability to take AI solutions from proof-of-concept through to stable, supportable production services. - Experience connecting systems via APIs, SDKs, webhooks, identity services, or automation tooling. - Working knowledge of enterprise security, privacy, compliance, and data governance practices. - Substantial hands-on experience administering or engineering enterprise SaaS, collaboration, productivity, search, automation, or AI platforms. - Real-world experience with platforms such as Slack, Google Workspace, Gemini for Workspace, Glean, Claude, ChatGPT, or similar enterprise AI tools. - Strong systems administration foundation: identity and access management, role-based permissions, configuration management, logging, monitoring, incident response, and change management. - Sharp troubleshooting ability along with strong technical writing, documentation, and stakeholder communication skills. - Capacity to lead through technical credibility and influence, rather than formal management authority. **Nice-to-Haves** - Familiarity with SSO, SCIM provisioning, OAuth, service accounts, delegated authorization, and enterprise identity providers. - Exposure to cloud platforms, infrastructure as code, source control, CI/CD, secrets management, and observability tools. - Development experience in Python, TypeScript, JavaScript, or similar languages. - Experience building MCP servers or integrating MCP-compatible clients and tools. - Experience running an enterprise AI platform or supporting an internal AI enablement initiative. - Familiarity with responsible AI practices, AI security frameworks, and model/agent evaluation methods. **Tools & Platforms** - Hands-on familiarity with several of: Slack, Google Workspace, Gemini for Workspace, Glean, Claude, and ChatGPT (or comparable enterprise products). - Practical experience with AI integration patterns involving MCP, function calling, APIs, SDKs, webhooks, enterprise search, data connectors, and identity services.