You will manage the AI Platform Engineer(s), set the technical standards for the AI Power User group's citizen development program, and serve as the connective tissue between business leadership, platform owners, and development teams. You will shape the multi-year AI architecture roadmap while also rolling up your sleeves to conduct architecture reviews, resolve blockers, and move use cases from concept to production. This is a role for someone who can think big and execute - and who understands that in an enterprise context, the quality of your governance is inseparable from the quality of your architecture.
What You'll Own
Strategy & Architecture
Define and own the enterprise AI integration strategy - identifying opportunities to embed intelligent automation, agentic workflows, predictive analytics, and generative AI capabilities across our core platforms
Develop and maintain reference architectures, design patterns, and the AI architecture decision log that governs how AI models connect to enterprise systems and what they are permitted to do
Consult on enterprise system architecture and implement best practices for the Enterprise Business Systems team to leverage in their day-to-day execution.
Lead Proof-of-Concept initiatives for new AI tools and platform-native AI features, evaluating them against build-vs-buy criteria before recommending adoption
Partner with business stakeholders to translate operational pain points into AI use cases with clear ROI framing and sequencing criteria
Contribute to our enterprise data strategy, ensuring AI initiatives are supported by clean, accessible, and well-governed data pipelines
Integration Architecture & Delivery
Design and own the Workato eMCP layer - the MCP governance model, persona-scoped token framework, workspace isolation strategy, and the single sanctioned action surface through which all AI agents write back to enterprise systems
Define integration patterns and standards for AI model connectivity (Claude, ChatGPT) to Salesforce, NetSuite, HiBob, and Jira - specifying what agents can read, what they can write, through which surfaces, and with what confirmation and audit requirements
Requirements: 8+ years of experience in enterprise solutions architecture, systems integration, or a closely related discipline - with a strong track record of designing and delivering production-grade integration platforms at scale
Deep hands-on expertise with Workato or a comparable enterprise iPaaS platform (MuleSoft, Boomi, Azure Integration Services) - including workspace design, governance configuration, and operational management
Demonstrated experience building and integrating across CRM (Salesforce preferred), ERP (NetSuite preferred), and iPaaS platforms at the enterprise level - in production, not just proof-of-concept
Hands-on experience designing or deploying AI/ML features in production enterprise environments - including at least one of: agentic AI systems, LLM-powered workflows, predictive analytics, or intelligent document processing
Strong command of integration patterns: REST/GraphQL APIs, event streaming, ETL/ELT pipelines, webhook-based automation, and API security best practices
Experience designing and enforcing integration governance: access control models, audit logging, approval workflows, and token management
Familiarity with Model Context Protocol (MCP) or direct experience connecting AI models to enterprise systems in a production context
Proven ability to lead distributed technical teams and communicate architecture clearly to both executive sponsors and engineering teams - you can hold a technical standard without becoming a bottleneck
Experience with the requisite AI-related Audit Management frameworks (ISO42001, ISO27001, SOC 2, etc.)
This position is open to all candidates.