Enterprise Systems Engineer V (DevOps)

Bitovi•Durham, NC

6d•$126,000 - $244,000

About The Position

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. 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. We are hiring a DevOps Bar Raiser — someone who raises the performance, standards, and platform maturity of the teams they work with.

Requirements

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.
Embracing AI as an essential capability.
Experience using AI and AI-related technologies, ready to thrive here.
Apply AI every day to business challenges - improving efficiency, contributing solutions, and driving results for your team, our company, and our customers.
Grow with AI by staying curious about new trends and best practices, and by sharing what you learn so others can benefit too.

Nice To Haves

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.

Responsibilities

Own the end‑to‑end lifecycle of Avalara's AI workflow infrastructure, with clear accountability for outcomes rather than task lists.
Technical owner for the infrastructure and core components that run n8n and related AI workflow tools—environments, CI/CD, containers, runtime clusters, storage, secrets, networking, and integrations—ensuring they are resilient, scalable, and cost‑effective.
Embed security, privacy, and compliance into how AI workflows are built and run: hardened baselines, secret management, network and IAM boundaries, and CI/CD guards that prevent unsafe changes from reaching production.
Define and drive SLOs, metrics, logging, and alerting for the AI workflow platform, turning incidents into systematic improvements that reduce MTTR and change failure rates over time.
Create and enforce reusable patterns (templates, reference pipelines, IaC modules, guardrails) so teams building on n8n and other tools follow consistent, auditable practices instead of bespoke one‑offs.
Partner with architecture, security, and platform teams to align AI workflow infra with Avalara's Software Maturity Model and engineering governance, moving the platform and guiding teams to higher levels of maturity.
Elevate how teams build and operate automations by mentoring engineers, codifying best practices, and using AI tools yourself to materially improve speed, quality, and reliability of the AI workflow platform.
Use AI to augment incident triage, anomaly detection, capacity forecasting, and change‑risk assessment for n8n and related infra—e.g., AI‑assisted log analysis, pattern detection in pipeline failures, and recommendation of remediation steps.
Apply AI tools to speed up design and implementation of IaC, automated CI/CD pipelines, security policies, and runbooks, while still exercising strong judgment and governance over generated artifacts.
Partner with integration and platform teams to enable AI‑driven orchestration patterns (for example, intelligent routing, adaptive retries, intelligent throttling) within n8n or surrounding services, where it meaningfully improves reliability or cost.
Share AI practices, patterns, and guardrails with engineers and citizen developers using the platform so AI materially improves outcomes (cycle time, incident reduction, automation coverage) instead of becoming ad‑hoc experimentation.