AI System Architect - US

Tufin is standing up a governed, enterprise-scale AI program that spans ChatGPT, Claude, Workato eMCP, and a growing ecosystem of third-party AI applications. The AI System Architect is the most senior technical role in this program — the person who defines the architecture, enforces the governance model, and owns the integration surface that every AI agent in the company operates through.

This role sits inside Enterprise Technology, reporting directly to the Head of Enterprise Technology. That placement is intentional. The AI System Architect is not a researcher, a prompt engineer, or a standalone AI strategist — they are an enterprise systems leader who happens to be building at the frontier of agentic AI. They own the AI integration strategy across Tufin's core platforms (Salesforce, NetSuite, Workato, HiBob, Jira), the MCP governance model, the persona-scoped token design, and the integration patterns that connect AI capabilities to those systems without creating point-to-point dependency risk.

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.

Define and own the enterprise AI integration strategy — identifying opportunities to embed intelligent automation, agentic workflows, predictive analytics, and generative AI capabilities across Tufin's 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 Tufin's enterprise data strategy, ensuring AI initiatives are supported by clean, accessible, and well-governed data pipelines

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

Design and oversee API strategies, event-driven architectures, and middleware patterns that support scalable AI feature delivery — including agentic workflows, intelligent data transformation, anomaly detection, and natural language interfaces layered onto ERP and CRM data

Collaborate with Engineering during build phases, conducting architecture reviews, providing hands-on guidance, and resolving complex technical blockers

Define non-functional requirements — latency, security, auditability, model drift monitoring — for AI components embedded in mission-critical business processes

Establish MLOps and LLMOps practices appropriate for Tufin's enterprise environment: model versioning, observability, and rollback procedures for production AI workloads

Translate Tufin's AI governance framework into enforceable runtime controls: confirmation gates, role-scoped permissions, audit trails, and rate limiting across all production agents

Own the AI intake process — the structured gate through which new AI use cases, agent deployments, and integration requests are reviewed, approved, and sequenced

Lead AI impact assessments for enterprise use cases, accounting for data privacy, regulatory compliance (GDPR, SOC 2, and applicable industry mandates), and responsible AI principles

Partner with Tufin's Security and Compliance teams and AI Governance Committee to define guardrails for agents operating with write access to critical systems — including human-in-the-loop checkpoints and audit trail requirements

Define the promotion criteria that citizen-built recipes must meet before the AI Platform Engineer can approve them for production, and hold that bar consistently across all value streams

Monitor for shadow AI and unauthorized usage — and treat its presence as an architectural signal, not just a policy violation

Manage and mentor the AI Platform Engineer(s) — setting technical direction, reviewing their work, and creating space for them to grow into the program's complexity

Set the technical standards and guardrails for the AI Power User group's citizen development program — defining what Power Users can build, on which platforms, with what approvals required before production promotion

Run architectural reviews for high-complexity citizen-built workflows and serve as the escalation point when the Platform Engineer identifies patterns outside established standards

Actively prevent shadow AI from taking root — not by blocking access, but by making the governed path so well-designed that it has no serious competition

Advise the Head of Enterprise Technology on AI integration strategy, platform evolution, and technology decisions as the enterprise AI tooling market continues to shift rapidly

Evaluate and recommend third-party AI tooling, LLM providers, and platform-native AI features — maintaining awareness of MCP ecosystem developments, Workato's AI platform roadmap, and the capabilities of the AI models Tufin has deployed

Maintain documentation standards and AI architecture protocols that satisfy both engineering teams and enterprise architecture review processes

Contribute to Tufin's AI governance framework as a living document, revising and extending it as new agent capabilities, regulatory signals, and organizational needs emerge

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.)

Hands-on experience with Workato's AI Hub and/or eMCP enterprise connector offerings

Experience with vector databases, RAG (retrieval-augmented generation) architectures, or fine-tuning workflows in an enterprise data context

Working knowledge of AI governance frameworks (NIST AI RMF, EU AI Act considerations), privacy controls, and secure SDLC practices

Relevant certifications in cloud platforms (AWS, Azure, GCP) or enterprise platforms (Salesforce, NetSuite, Workato)

Experience designing citizen development programs — defining guardrails, review processes, and promotion criteria for non-engineer builders

Background in network security, cybersecurity, or compliance-adjacent enterprise environments — familiarity with Tufin's domain is a meaningful advantage

Experience in a regulated industry (financial services, healthcare, or manufacturing) where AI governance requirements are non-negotiable

You design for the long run — your architectures are opinionated enough to prevent sprawl and flexible enough to absorb what comes next

You govern by making the right path easy, not by making the wrong path hard — the best control is one that people follow because it serves them

You can hold a technical position in a room of non-technical executives and explain why it matters without losing either the nuance or the audience

You review other people's work with the same rigor you apply to your own — and you give feedback that makes people better, not just feedback that makes things compliant

You treat shadow AI as a design failure, not a user problem — if the governed path isn't being used, that's an architectural signal worth investigating

You think big and execute — strategy and hands-on delivery are not separate modes for you

You flag risks early, document decisions thoroughly, and operate with the understanding that the choices you make now will be someone else's production system for years