DevOps Engineer

About the role

DEVOPS ENGINEER

Location: Austin, Texas Schedule: Full-time Travel: Less than 5% Education: Bachelor’s degree in Computer Science, Engineering, Information Systems, or equivalent practical experience Experience: 5+ years in DevOps, Platform Engineering, Site Reliability Engineering, or closely related infrastructure roles

Please note: This position is not eligible for employment sponsorship. BusPatrol is unable to sponsor or take over sponsorship of an employment visa for this role at this time.

JOB OVERVIEW

At BusPatrol, we are transforming student transportation safety through modern cloud platforms, secure automation, and real-time operational excellence. We are seeking a hands-on Sr DevOps Engineer to help build and evolve the engineering paved road across infrastructure, CI/CD, observability, cloud security, and developer workflows—with a strong focus on Kubernetes-based platforms and production MLOps.

Kubernetes and MLOps are important parts of this role, not just adjacent experience. You will help make containerized platforms reliable and secure, and enable machine-learning workloads to move from development into production through repeatable deployment, monitoring, and operational practices. Candidates should bring substantial hands-on experience in Kubernetes, MLOps, or both; practical strength in one area and a willingness to contribute across the other are important.

This role is ideal for someone who enjoys turning manual, inconsistent processes into reliable systems that make engineering teams faster, safer, and more effective. You will play a key role in BusPatrol’s platform modernization and AI-native SDLC journey, supporting secure, spec-driven engineering workflows, strengthening CI/CD and policy gates, improving delivery reliability, and helping teams adopt repeatable AI-enabled development practices without compromising security, compliance, or production readiness.

KEY RESPONSIBILITIES

- Design, provision, and maintain cloud infrastructure using infrastructure-as-code patterns, reusable modules, and secure platform standards.

- Build, operate, and improve Kubernetes platforms and workloads, including cluster and workload reliability, deployment patterns, scaling, upgrades, and operational readiness.

- Help establish repeatable deployment and operational patterns for machine-learning workloads, including model-serving or inference services and, where applicable, training and data-processing workloads.

- Partner with MLOps and engineering teams to make model delivery secure and production-ready, with appropriate automation, observability, versioning, and rollback practices.

- Help define and improve the engineering paved road for service delivery, release patterns, environment consistency, and platform guardrails across conventional and AI/ML workloads.

- Build, maintain, and harden CI/CD pipelines that support safe build, test, release, promotion, rollback, and change visibility across services and environments.