Automation Engineer V

Avalara is an AI-first company that is redefining the relationship between tax and technology. They are seeking an Automation Engineer V to establish and engineer an enterprise-grade AI automation and transformation ecosystem, focusing on improving operational efficiency and reducing manual processes across the organization.

Responsibilities:

• 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
• Promote best practices for prompt engineering, agent orchestration, human-in-the-loop workflows, and responsible AI automation
• Elevate automation engineering capability across the organization, including AI fluency and adoption

Requirements:

• B.S. in Computer Science, Engineering, or a related field (required)
• 10+ years of experience in enterprise automation, workflow engineering, integration engineering, or platform architecture
• Deep hands-on expertise in n8n or Boomi, including building and orchestrating AI-enabled workflows, agents, and cross-functional business automations
• Experience designing API-first and event-driven architectures, including integration of AI/ML services and agent-based systems
• Strong understanding of REST, webhooks, OAuth, JWT, and API security, along with secure integration of AI services and model endpoints
• Experience implementing CI/CD for automation or integration platforms, including deployment and versioning strategies for AI workflows, prompts, and models
• Cloud experience (AWS, Azure, or GCP), including AI/ML services (e.g., Bedrock, Azure OpenAI) and scalable model integration patterns
• Familiarity with LLMs, prompt engineering, AI agents, and orchestration frameworks, and how they apply to enterprise automation
• Experience with observability and monitoring, including AI-specific considerations (latency, cost, accuracy, drift)
• Proven ability to influence architecture and technical direction at scale, including driving adoption of AI-powered automation, process transformation, and intelligent orchestration patterns