Automation Engineer V

Avalara

10h•Remote

About The Position

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. Leaving every platform, process, and engineering practice stronger than it was before. This role does not only build automation, it transforms how work is engineered, automated, governed, and scaled across Avalara.

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
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: Design and implement AI-enabled workflows and transformation patterns that materially improve speed, automation, and scale
Use AI tools to optimize development productivity, workflow diagnostics, process analysis, and root-cause investigation
Identify high-value AI automation opportunities tied to efficiency, reliability, employee experience, or customer impact
Apply AI responsibly with appropriate governance, security, and risk considerations
Elevate AI capability across the automation team by sharing best practices and driving measurable adoption
This role must demonstrate applied AI impact — not casual tool usage — and quantify improvements enabled by AI-driven automation and transformation.

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
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