Enterprise Systems Engineer V

Avalara is scaling an AI‑first automation ecosystem where tools like n8n and other AI workflow platforms orchestrate critical business processes across tax, finance, operations, and internal productivity. To do this safely and at scale, we need a DevOps leader who will design, set up, deploy, and run the underlying infrastructure and all components around it—including governance, security, observability, and reliability—for these AI workflow tools. 

This Enterprise Systems Engineer , playing a senior DevOps Engineer role ,exists to build a secure, enterprise‑grade AI workflow platform that teams can trust for high‑impact automations, reducing manual work, shortening cycle times, and improving uptime for AI‑powered workflows across Avalara. 

• For teams: You provide a hardened, self‑service platform for AI workflows (starting with n8n) so product, operations, and IT teams can ship automations faster without reinventing infra or bypassing governance. 

• For the business: You increase operational reliability and speed by making AI workflows easy to change, safe to deploy, observable in production, and secure by design, directly improving SMM/DevOps maturity and audit readiness. 

• For customers: By stabilizing and scaling AI‑driven internal and product workflows, you reduce incidents, improve response times, and enable smarter, more automated experiences across Avalara’s tax compliance platform. 

We are hiring a DevOps Bar Raiser — someone who raises the performance, standards, and platform maturity of the teams they work with. This role is expected to improve how systems are architected, automated, deployed, and operated by enhancing engineering judgment, operational excellence, and cross-functional delivery. As part of an AI-first company, this role must also demonstrate applied AI impact by embedding AI into DevOps workflows in ways that measurably improve speed, reliability, scalability, and overall engineering effectiveness. 

• Track record of raising standards for reliability, security, or automation through documentation, mentoring, or governance—leaving systems and processes stronger than you found them. 

• Ability to communicate clearly with engineering, security, operations, and business stakeholders, explaining trade‑offs and setting realistic expectations in non‑jargon language. 

• Experience with mentoring and providing support for citizen developers who are new to agentic AI and automation concepts.   

If you are excited about building and running secure, AI‑ready workflow infrastructure and acting as a Bar Raiser for DevOps and AI at Avalara, we’d love to meet you.

• n8n and at least one additional AI workflow tool are running on a governed, multi‑environment platform (dev/test/prod) with standardized CI/CD, IaC, and security controls in place. 

• Documented uplift in SMM/DevOps maturity scores for the AI workflow platform along CI/CD, security, IaC, and observability dimensions, with evidence used in audits and ARB reviews. 

• Multiple teams actively building on the platform using standardized patterns and templates, with data showing shorter time‑to‑production and reduced manual operations for AI workflow changes. 

• Peer and leadership feedback, plus concrete examples, show you have raised standards for DevOps, automation, citizen developer adoption, and AI usage (e.g., documented patterns, training sessions, or platform improvements that others now rely on). 

Avalara is an AI-first company; this role must show applied AI impact, not casual tool usage. As a Senior DevOps Engineer for AI workflows, you are expected to leverage deep DevOps and platform engineering expertise to integrate AI into the software delivery and operations lifecycle in meaningful, measurable ways. This role should elevate how teams use AI across automation, observability, reliability, and release engineering, delivering tangible improvements in speed, stability, scalability, and operational excellence. 

A successful candidate will 

• • Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field. 

• • 5–8+ years in DevOps, SRE, or platform engineering for SaaS or large‑scale distributed systems, with direct ownership of production environments. 

• • Strong experience with at least one major cloud provider (AWS, Azure, or GCP), including VPC design, security groups, load balancers, and managed Kubernetes (EKS/AKS/GKE) or equivalent container orchestration. 

• • Deep hands‑on use of Infrastructure as Code (Terraform or equivalent) to manage multi‑environment infra and platform services. 

• • Proven ownership of CI/CD pipelines (GitLab CI/CD or similar), including automated testing, security scanning, and artifact management for complex services or platforms. 

• • Solid understanding of Linux and/or Windows, networking fundamentals (DNS, TLS, routing, firewalls), and secure secret management practices. 

• • Hands‑on experience with logging and monitoring stacks (e.g., Sumo Logic, Splunk, Prometheus, Grafana, or equivalents) and defining meaningful SLOs and alerts for production systems. 

• • Demonstrated experience running or supporting multi‑tenant or shared platforms used by multiple teams (internal developer platforms, workflow/orchestration tools, or integration platforms). 

• • Evidence of using AI tools in day‑to‑day engineering or operations (not just experimentation) with clear impact on speed, reliability, or quality.