AI System Architect - US

Tufin•Boston, MA

2d•Onsite

About The Position

Tufin is establishing a governed, enterprise-scale AI program that encompasses ChatGPT, Claude, Workato eMCP, and an expanding array of third-party AI applications. The AI System Architect is the most senior technical role within this program, responsible for defining the architecture, enforcing the governance model, and owning the integration surface through which all AI agents in the company operate. This role is situated within Enterprise Technology, reporting directly to the Head of Enterprise Technology. The AI System Architect is not a researcher, prompt engineer, or standalone AI strategist, but rather an enterprise systems leader focused on building at the forefront of agentic AI. Key responsibilities include owning the AI integration strategy across Tufin's core platforms (Salesforce, NetSuite, Workato, HiBob, Jira), the MCP governance model, persona-scoped token design, and integration patterns that connect AI capabilities to these systems without introducing point-to-point dependency risks. The role involves managing AI Platform Engineers, setting technical standards for the AI Power User group's citizen development program, and acting as a liaison between business leadership, platform owners, and development teams. The individual will shape the multi-year AI architecture roadmap while also actively participating in architecture reviews, resolving blockers, and moving use cases from concept to production. This position requires a candidate who can think strategically and execute effectively, understanding that in an enterprise setting, the quality of governance is intrinsically linked to the quality of the architecture.

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.
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.
Experience with requisite AI-related Audit Management frameworks (ISO42001, ISO27001, SOC 2, etc.).

Nice To Haves

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.

Responsibilities

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 their permitted actions.
Consult on enterprise system architecture and implement best practices for the Enterprise Business Systems team.
Lead Proof-of-Concept initiatives for new AI tools and platform-native AI features, evaluating them against build-vs-buy criteria.
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, including the MCP governance model, persona-scoped token framework, workspace isolation strategy, and the single sanctioned action surface for AI agents writing back to enterprise systems.
Define integration patterns and standards for AI model connectivity (Claude, ChatGPT) to Salesforce, NetSuite, HiBob, and Jira, specifying read/write permissions, surfaces, and confirmation/audit requirements.
Design and oversee API strategies, event-driven architectures, and middleware patterns for scalable AI feature delivery.
Collaborate with Engineering during build phases, conducting architecture reviews, providing guidance, and resolving technical blockers.
Define non-functional requirements (latency, security, auditability, model drift monitoring) for AI components in mission-critical business processes.
Establish MLOps and LLMOps practices for enterprise environments: model versioning, observability, and rollback procedures.
Translate Tufin's AI governance framework into enforceable runtime controls: confirmation gates, role-scoped permissions, audit trails, and rate limiting.
Own the AI intake process for reviewing, approving, and sequencing new AI use cases, agent deployments, and integration requests.
Lead AI impact assessments for enterprise use cases, considering data privacy, regulatory compliance (GDPR, SOC 2), and responsible AI principles.
Partner with Security, Compliance, and the AI Governance Committee to define guardrails for agents with write access to critical systems.
Define promotion criteria for citizen-built recipes before AI Platform Engineer approval for production.
Monitor for shadow AI and unauthorized usage, treating its presence as an architectural signal.
Manage and mentor AI Platform Engineer(s), setting technical direction and reviewing their work.
Set technical standards and guardrails for the AI Power User group's citizen development program.
Run architectural reviews for high-complexity citizen-built workflows and serve as an escalation point.
Actively prevent shadow AI by making the governed path well-designed and user-friendly.
Advise the Head of Enterprise Technology on AI integration strategy, platform evolution, and technology decisions.
Evaluate and recommend third-party AI tooling, LLM providers, and platform-native AI features.
Maintain documentation standards and AI architecture protocols.
Contribute to Tufin's AI governance framework as a living document.