Bitovi

Enterprise System Engineer V (DevOps)

Remote DevOps Engineer role with clear candidate location fit.

PostedJul 23, 2026

Eligible countries1 accepted country

Seniority signalSenior

Work settingRemote

Accepted candidate locations

USA

AWS Azure CI/CD GCP Kubernetes

Role overview

Enterprise System Engineer V (DevOps)

Requirements and responsibilities

Readable role content extracted into sections for faster review.

How This Role Elevates 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.

Bar Raiser Expectations

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.

What Your Responsibilities Will Be

Platform Ownership – AI Workflow Infra (incl. n8n): You are the 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.
Secure‑by‑Design Governance: You 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.
Operational Reliability & Observability: You 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.
Standardization & Reuse for AI Workflows: You 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.
Maturity & Governance Alignment (SMM / ARB): You 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.
Bar Raiser for DevOps & AI‑First Ways of Working: You 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.

12‑Month Success Signals

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.

12‑Month Success Signals

Measurable reductions in incident frequency and MTTR for AI workflow infrastructure, along with reduced deployment failures or rollbacks for n8n‑backed automations.

12‑Month Success Signals

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.

12‑Month Success Signals

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.

12‑Month Success Signals

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).

A successful candidate will

Design AI‑Enhanced Operations for the 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.
Accelerate Infra and Automation Delivery with AI
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.
Embed AI into the Platform Experience Itself
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.
Raise AI Maturity Across Teams (Bar Raiser)
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.

Qualifications

Education & ExperienceBachelor'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.
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.
Core Technical ExpertiseStrong 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.
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.
Platform & AI Workflow MindsetDemonstrated 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.
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.

Details

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.

Avalara is an AI-first Company

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.

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