Principal Architect - Enterprise AI
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AvalaraEnterprise AI
Full-time remote opportunity with Avalara based in the United States of AmericaFull-TimePrincipal
SalaryColorado $153,300-$203,100 (annually); Washington $153,300-$279,600 (annually); California $153,300-$306,300 (annually); NYC $185,500-$306,300 (annually)
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Job Details
- Experience
- 10+ years of experience in enterprise, solution, or platform architecture
- Required Skills
- AWSGCPAzureRESTful APIs
Requirements
- Bachelor's degree in Computer Science, Engineering, or equivalent practical experience.
- 10+ years of experience in enterprise, solution, or platform architecture.
- Experience with architecture standards, reference patterns, and governance frameworks.
- Platform architecture experience across integrations, data, cloud infrastructure, APIs, identity, and security.
- Hands-on experience building and prototyping with modern AI/LLM tools, APIs, and agent frameworks.
- Experience with enterprise architecture frameworks such as TOGAF or equivalent.
- Experience with AWS, Azure, or GCP and associated AI/ML services.
- Experience guiding teams and executive decision-making.
- Experience with AI enablement programs, workshops, or communities of practice.
- Experience architecting automation, integration, or orchestration platforms such as n8n, Boomi, MuleSoft, or Workato.
- Public writing, speaking, or open-source contributions related to applied AI.
Responsibilities
- Define and maintain enterprise architecture standards, reference patterns, and governance guardrails for AI-enabled solutions moving from pilot to production.
- Establish decision models for build, buy, or integrate choices; model and vendor selection; data access; security; and responsible AI adoption.
- Design scalable architecture patterns for AI-enabled workflows and agentic processes across data, APIs, identity, security, and cloud platforms.
- Test new AI models, LLMs, agent frameworks, and tools to inform enterprise adoption.
- Design proof-of-concepts, define success criteria, and guide solutions from pilot to production.
- Lead architecture reviews and communicate trade-offs, risks, and growth to partners.
- Partner with teams to translate needs into governed, implementation-ready solutions.
- Support teams through demos, workshops, and reusable templates to support safe AI adoption.
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