Automation Engineer V

As Avalara scales its AI-first enterprise systems, workflows, and automation footprint, we must reimagine how work is automated, orchestrated, and continuously improved across every function. This role exists to establish and engineer an enterprise-grade AI automation and transformation ecosystem, with n8n as a core platform, enabling faster process transformation, intelligent workflow execution, and measurable gains in productivity, quality, and operational scale. 

This is a high-impact individual contributor role responsible for elevating automation standards, reducing manual work and process friction, and enabling AI-powered transformation across the organization. 

This role strengthens Avalara’s enterprise automation and transformation capability by establishing scalable, secure, and measurable AI orchestration standards across the organization. 

This AI Automation Engineer will: 

• Improve operational efficiency and process quality by engineering resilient AI automation solutions and governed platform standards 

• Accelerate business transformation and reduce cycle time through reusable automation patterns, CI/CD enablement, and standardized development frameworks 

• Enhance employee and customer experience by removing manual bottlenecks, reducing process defects, and increasing AI workflow intelligence 

• Advance Avalara’s AI-first execution model by embedding agentic automation, intelligent decisioning, and data-driven observability into enterprise workflows 

As a Bar Raiser, this role is expected to elevate the capability and execution rigor of the entire automation and transformation function, not simply contribute as a senior engineer. This includes: 

• Holding high standards for quality, ownership, reliability, and accountability 

• Using metrics (automation adoption, cycle-time reduction, SLAs, SLOs, MTTR, cost per workflow) to guide platform and architectural decisions 

• Simplifying complex business processes into scalable, governed, and maintainable automations 

• Mentoring engineers and contractors to raise automation maturity and talent density 

• Challenging technical assumptions constructively and driving measurable improvement 

• Own the enterprise-wide AI automation and transformation strategy with n8n as a core orchestration platform 

• Architect scalable, secure, and resilient automation solutions that eliminate manual effort, improve process quality, and reduce operational friction 

• Design hybrid patterns leveraging n8n/Boomi, APIs, event-driven systems, and AI agents to improve reuse, enable intelligent decisioning, and reduce time-to-value 

• Define patterns for embedding AI-driven workflows (LLMs, agents, and decision engines) into business processes across functions 

• Lead architecture reviews and drive best-in-class automation design standards, including AI-assisted design patterns and governance 

• Serve as a technical owner for n8n and AI workflow automation initiatives 

• Define environment strategy (dev/test/prod), CI/CD pipelines, and governance models to reduce deployment risk and increase release velocity 

• Enable AI-powered workflow capabilities within n8n (e.g., agent orchestration, prompt management, model integrations, human-in-the-loop controls) 

• Implement workflow standards, logging, monitoring, and reliability guardrails to improve MTTR, uptime, and automation trust 

• Incorporate AI-assisted monitoring, anomaly detection, and intelligent alerting to proactively detect failures, drift, and degraded workflow performance 

• Ensure platform scalability, fault tolerance, and high availability for both deterministic and AI-driven workflows 

• Establish enterprise automation development standards and best practices 

• Define exception-handling frameworks, retry strategies, naming conventions, security protocols, and approval patterns that reduce production defects 

• Create reusable templates, accelerators, and automation design patterns, including AI agent templates and reusable prompt frameworks 

• Define governance for AI usage (model selection, cost controls, prompt/version management, data privacy, and auditability) 

• Introduce code review processes and quality gates that increase execution rigor, including AI workflow validation and evaluation standards 

• Operate as a player-coach — hands-on while mentoring engineers 

• Guide contractor and external implementation teams to ensure quality and standards adherence 

• Lead technical design sessions, workflow reviews, and post-incident reviews 

• Mentor teams on designing, building, and operationalizing AI agents and autonomous workflows within business processes 

Within the first 12 months, this role will have: 

• Enabled AI-native automation capabilities, including production-grade AI agents integrated into core workflows 

• Improved automation reliability and reduced workflow failure rates and/or MTTR 

• Standardized automation patterns, including AI-enabled orchestration patterns, adopted across new transformation initiatives 

• Implemented measurable observability, including AI performance, business impact, and cost metrics 

• Delivered AI-driven automation use cases that reduce manual effort, improve decision-making, or shorten process cycle time across multiple functions 

• Raised execution standards across the automation team through mentorship, technical leadership, and AI capability development 

As an AI-first company, Avalara expects this role to embed AI into how automation and transformation work is designed and executed. 

This role will: