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Ai System Architect Jobs (NOW HIRING)

Tufin

AI System Architect - US

Boston, MA · On-site +1

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 ...

InterSources Inc

Cube AI System Architect

Pennington, NJ · On-site

$245K/yr

They are seeking a Systems Architect to lead the design and integration of the Cube AI vendor product into the Bank's enterprise ecosystem, focusing on scalability, security, and compliance.

Sandisk

Senior Principal AI System Architect

Milpitas, CA · On-site

$194K - $322K/yr

A System Architect focusing on advanced memory technology and AI Inference solution infrastructure will lead the definition of system level solutions with emphasis on compute memory bottlenecks ...

SK hynix America

AI/HPC System Architect

San Jose, CA · On-site

$284K/yr

Job Title: AI/HPC System Architect Office Location: San Jose, CA Job Type: Full-Time Work Model: Onsite Job Summary: As an AI/HPC System Architect, your primary responsibility will be to ...

YPOG

$104 - $139/hr

Uns ist wichtig, in cross-funktionalen Teams einzigartige Legal AI-Produkte zu entwickeln und den ... mit in Form von KI-Systemen * Fachliche Konzeption von Prompting-, Retrieval- und ...

SK hynix America

AI/HPC System Architect

San Jose, CA · On-site

$155K - $255K/yr

AI/HPC System Architect Office Location: San Jose, CA Job Type: Full-Time Work Model: Onsite Job Summary: As an AI/HPC System Architect, your primary responsibility will be to conceptualize the ...

Escalon Services, LLC.

System Architect

Santa Monica, CA · On-site

$140K - $160K/yr

System Architect Application Deadline: 30 September 2026 Department: Recruiting Done Employment ... Collaborate with software, hardware, AI/ML, robotics, and product teams throughout the development ...

Escalon Services, Inc.

System Architect

Santa Monica, CA · On-site

$140 - $160/hr

System Architect Application Deadline: 30 September 2026 Department: Recruiting Done Employment ... Collaborate with software, hardware, AI/ML, robotics, and product teams throughout the development ...

SK hynix America

AI System Engineer

San Jose, CA · On-site

$117K - $160K/yr

AI System Engineer Office Location: San Jose, CA Job Type: Full-Time Work Model: Onsite Job Summary ... Design and develop next-generation memory solution architecture and hardware prototypes for energy ...

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Ai System Architect information

See salary details

$86.5K

$224.3K

$243.5K

How much do ai system architect jobs pay per year?

As of Sep 5, 2026, the average yearly pay for ai system architect in the United States is $224,334.00, according to ZipRecruiter salary data. Most workers in this role earn between $243,000.00 and $243,000.00 per year, depending on experience, location, and employer.

What is an AI system architect?

AI System Architects are professionals who design, plan, and oversee the development of artificial intelligence systems within organizations. They are responsible for creating the overall architecture for AI solutions, ensuring that different technologies, algorithms, and data sources work together seamlessly. AI System Architects collaborate with data scientists, engineers, and business stakeholders to implement scalable and effective AI-driven products and services. Their role often includes evaluating new technologies, setting best practices, and ensuring the security and scalability of AI systems.

How does an AI system architect typically collaborate with data scientists and engineers during a project?

An AI System Architect works closely with data scientists to understand the requirements and constraints of AI models and ensures that system infrastructure supports their needs. They collaborate with engineers to design scalable, robust architectures and oversee the integration of AI components into existing systems. Regular communication and joint problem-solving are essential, as architects bridge the gap between high-level AI goals and practical implementation, often facilitating technical discussions and making critical design decisions.

What are the key skills and qualifications needed to thrive as an AI system architect, and why are they important?

To thrive as an AI System Architect, you need a deep understanding of machine learning, distributed systems, data engineering, and typically a degree in computer science or a related field. Familiarity with cloud platforms (like AWS, GCP, or Azure), AI frameworks (such as TensorFlow or PyTorch), and experience with system design are essential, along with relevant certifications. Strong problem-solving skills, strategic thinking, and effective communication set top candidates apart. These skills ensure the architect can design scalable, efficient, and robust AI solutions that align with business objectives and technical requirements.

What is the difference between Ai System Architect vs Data Scientist?

AspectAi System ArchitectData Scientist
Required CredentialsBachelor's or master's in CS, AI, or related fields; certifications in AI/MLBachelor's or master's in CS, Statistics, or related fields; certifications in data analysis or ML
Work EnvironmentDesigning AI systems, collaborating with engineers, focusing on architectureAnalyzing data, building models, interpreting results
Employer & Industry UsageTech companies, AI-focused firms, R&D departmentsTech, finance, healthcare, marketing, research organizations

While both roles require knowledge of AI and ML, an Ai System Architect primarily designs and oversees AI system architecture, ensuring integration and scalability. In contrast, a Data Scientist focuses on analyzing data, building models, and deriving insights. The roles often collaborate but differ in their core responsibilities and focus areas.

How do you become an AI system architect?

To become an AI system architect, one typically needs a strong background in computer science, software engineering, or related fields, along with expertise in artificial intelligence, machine learning, and system design. Gaining experience through relevant projects, obtaining certifications in AI or cloud platforms, and developing skills in programming languages like Python and tools such as TensorFlow or PyTorch are also important steps.
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Ai System Architect jobs near you
Infographic showing various Ai System Architect job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 84% Full Time, 13% Part Time, and 2% Contract. Highlights an 91% Physical, 2% Hybrid, and 7% Remote job distribution, with an average salary of $224,334 per year, or $107.9 per hour.

AI System Architect - US

Tufin

Boston, MA • On-site, Remote

Full-time

Re-posted 16 days ago

Key responsibilities

  • Define and own the enterprise AI integration strategy, including developing reference architectures and design patterns.

  • Design and oversee the integration patterns, standards, and API strategies for connecting AI models to enterprise systems such as Salesforce, NetSuite, HiBob, and Jira.

  • Translate the AI governance framework into runtime controls, lead the AI intake process, and ensure compliance with data privacy, regulatory, and responsible AI principles.

Job description

Description
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.
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 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

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
  • 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

Governance & Risk
  • 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

Team Leadership & Citizen Development
  • 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

Strategic Technical Leadership
  • 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

Requirements
What You Bring
Required
  • 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.)

Preferred
  • 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

How You Lead
  • 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

Apply