Principal Automation Engineer

Bitovi•Durham, NC

About The Position

As Avalara continues to scale its AI-first enterprise systems, workflows, and automation footprint, we must ensure our AI automation and transformation architecture is intentional, governed, and built for long-term scale. This role exists to define and drive enterprise architecture for AI automation and business transformation, reduce fragmentation across workflow, agent, and integration solutions, and enable faster, more reliable delivery of automation initiatives that support business growth and operational excellence. This is a senior individual contributor lead role responsible for shaping scalable AI automation architectures, improving solution resilience and governance, and ensuring platform strategy supports measurable business outcomes across the company. This role will work extensively with business stakeholders, AI Automation engineers and BSAs to turn business transformation opportunities into durable, enterprise-grade architectural patterns. This role directly strengthens Avalara's enterprise AI automation and transformation ecosystem by increasing architectural rigor, scalability, reuse, and long-term sustainability across business workflows, platforms, and enterprise systems. As a Bar Raiser, this role is expected to elevate the performance of the organization — not simply maintain it. This includes: Holding high standards for architectural clarity, quality, and accountability Using data and measurable impact to guide enterprise technical decisions Simplifying complex, cross-system and cross-workflow challenges into scalable and sustainable designs Guiding AI Automation Engineers, BSAs, and solution designers to raise automation maturity and talent density Challenging assumptions respectfully and improving execution rigor across teams Leaving every system, process, and architectural framework stronger than it was before. This role does not only design solutions — it improves how the organization thinks about, governs, and executes AI automation and transformation at scale.

Requirements

B.S. in Computer Science or Engineering (required)
10+ years of experience in solution, enterprise, or platform architecture roles
Strong hands-on experience with modern automation and integration platforms (iPaaS and workflow orchestration tools)
Experience architecting for platforms such as n8n, Boomi or similar platforms like mulesoft/workato, or comparable automation ecosystems
Experience designing AI-enabled, API-driven, and event-based architectures for enterprise workflows and transformation initiatives
Deep understanding of automation patterns, middleware, data transformation, agent orchestration, and human-in-the-loop workflow design
Strong knowledge of REST, webhooks, OAuth, SSO, API security, and governance best practices for AI-enabled systems
Experience with cloud-native architectures and AI/ML services (AWS, Azure, or GCP)
Proven ability to influence enterprise-wide technical direction and partner effectively with AI Automation Engineers, BSAs, and cross-functional leaders
Demonstrating applied AI impact — not casual familiarity — is required.

Responsibilities

Lead evaluation of AI automation, agentic workflow, and transformation initiatives across the organization
Define architectural decision frameworks and long-term platform strategy for n8n, Boomi, APIs, event-driven orchestration, and AI services to reduce duplication and increase reuse
Establish governance guardrails for AI-enabled workflows, agents, prompts, models, data flows, and human-in-the-loop controls to ensure scalability, reliability, auditability, and responsible use
Anticipate growth, compliance, security, and operational risks across automation platforms and transformation programs before they materialize
Influence cross-functional roadmaps with AI Automation Engineers, BSAs, and business leaders based on architectural trade-offs, platform fit, and measurable outcomes
Translate enterprise capability needs into clear, implementable technical designs that accelerate AI automation delivery
Create architectural blueprints for intelligent workflows, agentic process patterns, and cross-system orchestration that reduce ambiguity and minimize rework
Design secure, scalable, and resilient automation patterns across SaaS systems, internal platforms, APIs, data ecosystems, and AI/ML services
Drive API-first, event-driven, and human-in-the-loop architecture standards that improve interoperability, control, and long-term maintainability
Lead architecture reviews and represent AI automation and transformation strategy in executive and cross-functional forums
Partner across business, platform, data, security, and engineering functions to create shared clarity from concept through execution for transformation initiatives
Work closely with AI Automation Engineers and BSAs to turn business process opportunities into governed, implementation-ready automation architectures
Communicate trade-offs, sequencing, and architectural decisions using data, business impact, risk, and cost considerations
Solve ambiguous, high-complexity challenges spanning workflows, systems, AI agents, process controls, and enterprise change
Define enterprise standards, reference architectures, documentation templates, and best practices for AI automation and transformation initiatives
Establish performance, reliability, observability, and cost-management benchmarks for deterministic and AI-enabled workflows
Reduce automation and architectural debt through proactive platform direction, reusable patterns, and rationalization of fragmented solutions
Improve resilience, scalability, governance, and long-term maintainability across automation platforms and cross-functional process architectures
Provide architectural oversight on high-impact AI automation and business transformation initiatives
Mentor AI Automation Engineers, BSAs, and solution designers, raising overall automation architecture maturity and cross-functional execution quality
Guide contractor and vendor alignment to enterprise standards for automation, agentic workflows, governance, and platform use
Elevate organizational AI automation capability through durable architectural principles, reusable patterns, and clear decision frameworks
Design and govern AI-enabled automation patterns, agentic workflows, and transformation architectures that improve speed, scale, and decision quality
Evaluate and incorporate AI-driven orchestration, prompt and model governance, and human-in-the-loop decision frameworks into enterprise architecture
Use AI tools to improve architectural documentation, process analysis, impact assessment, and scenario modeling
Identify AI opportunities tied to measurable business outcomes (efficiency, cycle-time reduction, reliability, employee experience, customer impact, risk reduction)
Apply AI responsibly, with attention to governance, security, compliance, auditability, and cost control