Buspatrol Sr DevOps Engineer at BusPatrol leads cloud infrastructure, Kubernetes platform, and MLOps development to enhance student transportation safety. Focuses on CI/CD, observability, and secure automation.
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.
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.
Qualification
5+ years of experience in DevOpsExperience with TerraformFor Kubernetes-focused candidatesFor MLOps-focused candidatesExperience supporting Kubernetes in AWSExperience with policy-as-code
Required
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.
Preferred
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
