Principal Enterprise Architect

Avalara; United States; On-site; USD 180,000 - 240,000; Full time

Job summary

Avalara is seeking a Principal Architect, Enterprise AI to define how AI is architected, governed, scaled, and adopted across the enterprise. This senior, hands-on role combines enterprise architecture with practical AI, LLMs, and automation expertise.

You will establish architecture standards, governance guardrails, and reusable patterns, while evaluating new models and platform capabilities for scalable production use.

Qualifications

• 10+ years in enterprise, platform, or solution architecture.
• Hands-on with AI/LLM technologies and architecting at scale.
• Familiarity with TOGAF or equivalent methodologies.
• Ability to prototype and communicate complex architectures.

Responsibilities

• Define enterprise AI architecture standards and governance guardrails.
• Lead architecture decisions across AI, data, cloud, security, and integrations.
• Design resilient, observable AI-enabled workflows at production scale.
• Evaluate emerging AI tools and map them to production patterns.

Skills

Enterprise architecture

AI/LLM technologies

Cloud architecture

Governance and guardrails

TOGAF familiarity

Education

Bachelor's degree in Computer Science/Engineering

Tools

n8n

Boomi

MuleSoft

Workato

Job description

Avalara is becoming an AI-first company, and we’re looking for a Principal Architect, Enterprise AI to help define how AI is architected, governed, scaled, and adopted across the enterprise.

This is a senior, hands-on architecture role for someone who combines deep enterprise/platform architecture expertise with genuine, current experience building with AI, LLMs, agent frameworks, and automation technologies.

You will establish the architecture standards, governance guardrails, decision frameworks, and reusable patterns that enable AI solutions to move successfully from experimentation and pilots into secure, scalable production systems. At the same time, you’ll stay close to the rapidly evolving AI landscape—personally testing new models, frameworks, tools, and platform capabilities and translating what matters into practical solutions for Avalara.

What You’ll Do

• Define and evolve enterprise AI architecture standards, reference patterns, design frameworks, and governance guardrails.
• Lead architecture decisions across AI, data, integrations, cloud infrastructure, identity, security, and enterprise platforms.
• Architect resilient, observable, secure, and cost-aware AI-enabled workflows and agentic systems at production scale.
• Establish practical decision frameworks for build vs. buy vs. integrate, model/vendor selection, data access, security, and governance.
• Track and personally evaluate emerging LLMs, agent frameworks, copilots, orchestration technologies, and AI platform capabilities across OpenAI, Anthropic, Google, Microsoft, AWS, and the broader AI ecosystem.
• Design and build proof-of-concepts and pilots against real business use cases, with clear success criteria and a path to production.
• Partner with platform, data, security, engineering, and enterprise architecture teams to move validated AI capabilities into scalable production environments.
• Work alongside AI Automation Engineers and Business Systems Analysts to translate complex business opportunities into governed, implementation-ready architectures.
• Lead architecture reviews and communicate technical trade-offs, risk, cost, and business impact to both engineering teams and executive stakeholders.
• Create reusable patterns, guidance, demos, and workshops that help teams across Avalara adopt AI effectively and responsibly.
• Mentor engineers, architects, and solution designers while raising the overall architectural maturity of Avalara’s AI initiatives.

We’re Looking For

• 10+ years of experience in enterprise, platform, or solution architecture, with experience defining architecture standards, reference patterns, and governance frameworks.
• Strong platform-level architecture experience across integrations, data, cloud infrastructure, security, and distributed enterprise systems.
• Familiarity with enterprise architecture practices such as TOGAF or equivalent methodologies, applied pragmatically.
• Hands‑on experience with modern AI/LLM technologies, APIs, and agent frameworks—you can prototype and build, not simply evaluate technology from a distance.
• Experience designing architectures involving AI agents, LLM‑powered applications, workflow automation, APIs, orchestration, and enterprise integrations.
• Experience with automation/integration platforms such as n8n, Boomi, MuleSoft, Workato, or comparable iPaaS/orchestration technologies.
• Strong experience with at least one major cloud platform: AWS, Azure, or GCP, including AI/ML services.
• Demonstrated ability to take emerging technology from idea → prototype → validated pilot → reusable production architecture.
• Strong judgment around scalability, reliability, observability, security, data privacy, governance, and cost.
• Ability to communicate complex architecture and emerging AI concepts clearly to technical, business, and executive audiences.
• A genuine habit of staying current with—and personally experimenting with—the rapidly changing AI ecosystem.
• B.S. in Computer Science, Engineering, or equivalent practical experience.

Nice to Have

• Experience leading internal AI enablement programs, workshops, architecture communities, or communities of practice.
• Public writing, speaking, open‑source contributions, or demonstrated thought leadership in applied AI.

What Success Looks Like

You won’t simply design individual AI solutions. You’ll help shape how Avalara thinks about, governs, architects, and executes AI adoption at enterprise scale.

Success means creating architectural standards teams actually use, rapidly identifying which emerging AI capabilities matter, turning promising ideas into production‑ready patterns, reducing architectural rework, and helping Avalara move faster with AI without compromising scalability, security, governance, or engineering rigor.