What You'll Do Role Summary 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. How This Role Elevates Avalara 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 Bar Raiser Expectations 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. What Your Responsibilities Will Be Enterprise AI Automation Leadership 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 Platform Ownership (n8n) 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 Standards & Governance 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 Technical Mentorship & Delivery Excellence 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 12-Month Success Signals 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 What You'll Need to be Successful AI Expectations 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. What You Bring 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 Pay Range Details The base pay range(s) below are provided in compliance with state specific laws. Pay ranges may be different in other locations. Colorado $126,000-$201,600 (annually) Washington $126,000-$222,800 (annually) California $126,000-$244,000 (annually) NYC $139,200-$244,000 (annually) The pay range above is the general base pay range for a successful candidate in the state listed. The successful candidate’s actual salary/wage may be based on various factors, such as geographic location, candidate experience and qualifications, as well as market and business considerations. This role is eligible for an annual bonus based on individual and company performance, depending on the terms of the applicable plan and the employee’s role. Avalara is an AI-first Company AI is embedded in our workflows, decision-making, and products. Success here requires embracing AI as an essential capability. You’ll bring experience using AI and AI-related technologies, ready to thrive here. You’ll apply AI every day to business challenges - improving efficiency, contributing solutions, and driving results for your team, our company, and our customers. You’ll grow with AI by staying curious about new trends and best practices, and by sharing what you learn so others can benefit too. How We'll Take Care of You Total Rewards In addition to a great compensation package, paid time off, and paid parental leave, many Avalara employees are eligible for bonuses. Health & Wellness Benefits vary by location but generally include private medical, life, and disability insurance. Inclusive culture and diversity Avalara strongly supports diversity, equity, and inclusion, and is committed to integrating them into our business practices and our organizational culture. We also have a total of 8 employee-run resource groups, each with senior leadership and exec sponsorship. What You Need To Know About Avalara We’re defining the relationship between tax and tech. We’ve already built an industry-leading cloud compliance platform, processing over 54 billion customer API calls and over 6.6 million tax returns a year. Our growth is real - we're a billion dollar business - and we’re not slowing down until we’ve achieved our mission - to be part of every transaction in the world. We’re bright, innovative, and disruptive, like the orange we love to wear. It captures our quirky spirit and optimistic mindset. It shows off the culture we’ve designed, that empowers our people to win. We’ve been different from day one. Join us, and your career will be too. We’re An Equal Opportunity Employer Supporting diversity and inclusion is a cornerstone of our company — we don’t want people to fit into our culture, but to enrich it. All qualified candidates will receive consideration for employment without regard to race, color, creed, religion, age, gender, national orientation, disability, sexual orientation, US Veteran status, or any other factor protected by law. If you require any reasonable adjustments during the recruitment process, please let us know. Enterprise AI Automation Leadership 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 Platform Ownership (n8n) 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 Standards & Governance 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 Technical Mentorship & Delivery Excellence 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 12-Month Success Signals 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 AI Expectations 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. What You Bring 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 Pay Range Details The base pay range(s) below are provided in compliance with state specific laws. Pay ranges may be different in other locations. Colorado $126,000-$201,600 (annually) Washington $126,000-$222,800 (annually) California $126,000-$244,000 (annually) NYC $139,200-$244,000 (annually) The pay range above is the general base pay range for a successful candidate in the state listed. The successful candidate’s actual salary/wage may be based on various factors, such as geographic location, candidate experience and qualifications, as well as market and business considerations. This role is eligible for an annual bonus based on individual and company performance, depending on the terms of the applicable plan and the employee’s role.
