Sr 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.

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.

• 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.

• Improve pipeline quality gates with automated security scanning, dependency checks, and policy enforcement where appropriate.

• Strengthen observability across applications, Kubernetes platforms, and infrastructure by standardizing logs, metrics, traces, dashboards, alerts, and operational runbooks.

• Partner with engineering teams to improve reliability, performance, recovery time, and production readiness for distributed and containerized services.

• Support secure AI-enabled software delivery by helping teams operationalize spec-driven workflows, AI-assisted PR practices, automated test generation, and controlled release-readiness checks.

• Contribute to AI/ML delivery systems that are secure, repeatable, observable, and aligned with BusPatrol guardrails for sensitive data, secrets, and production change control.

• Improve developer experience through self-service tooling, templates, documentation, and workflow automation that reduce toil and help teams ship with confidence.

• Help make the easiest path the safest and most repeatable one for engineers across product and platform teams.

• Participate in incident response, root-cause analysis, and continuous improvement efforts.

• Use operational data and engineering feedback to reduce recurring issues, strengthen controls, and improve the overall delivery system.

• Collaborate closely with software engineering, security, architecture, MLOps, and IT teams to deliver scalable internal platforms and high-trust operational practices.

• Connect day-to-day DevOps execution with BusPatrol’s broader modernization, reliability, and AI adoption goals.

• 5+ years of experience in DevOps, Platform Engineering, SRE, or cloud infrastructure roles.

• Hands-on experience with AWS and modern infrastructure automation practices.

• Experience with Terraform, AWS CDK, or comparable infrastructure-as-code tooling.

• Experience building and maintaining CI/CD pipelines in GitHub Actions or similar systems.

• Substantial hands-on experience with Kubernetes in production, MLOps, or both. Candidates should be able to demonstrate depth in at least one of these areas and the ability to work effectively with the other.

• For Kubernetes-focused candidates: experience deploying and operating containerized workloads, troubleshooting production issues, and improving reliability, scaling, and observability.

• For MLOps-focused candidates: experience operationalizing machine-learning workloads or model services, including deployment automation and production monitoring.

• Working knowledge of observability tooling, monitoring, and production operations.

• Familiarity with cloud security controls, secrets handling, branch protections, and secure release workflows.

• Ability to work effectively with engineers, security partners, and technical stakeholders across teams and time zones.

• Strong written documentation habits and an automation-first mindset.

• Hands-on experience across both Kubernetes platform operations and MLOps.

• Experience supporting Kubernetes in AWS, such as EKS, or a comparable managed Kubernetes environment.

• Experience supporting platform modernization from legacy systems toward service-oriented or microservices-based architectures.

• Experience with policy-as-code, automated security gates, or compliance-oriented controls in engineering workflows.

• Exposure to AI-assisted engineering workflows, spec-driven development, or AI-enabled SDLC tooling.

• Experience improving developer self-service through templates, paved roads, internal platforms, or golden-path patterns.

• Kubernetes platform operations and container orchestration

• MLOps and production ML workload enablement

• Infrastructure as Code

• GitOps and release automation

• CI/CD pipeline engineering

• AWS platform operations

• Observability and incident response

• Cloud security and policy enforcement

• Developer experience and platform enablement

• Documentation and runbook creation

• AI workflow enablement with governance

• Cross-functional technical collaboration