Sr DevOps Engineer

BusPatrol•Austin, TX

About The Position

BusPatrol is seeking a hands-on Sr DevOps Engineer to build and evolve its engineering paved road across infrastructure, CI/CD, observability, cloud security, and developer workflows, with a strong focus on Kubernetes-based platforms and production MLOps. The role helps make containerized platforms reliable and secure and enables machine-learning workloads to move from development into production through repeatable deployment, monitoring, and operational practices. Candidates should have substantial hands-on experience in Kubernetes, MLOps, or both, with practical strength in one area and a willingness to contribute across the other. The engineer will turn manual and inconsistent processes into reliable systems that help engineering teams work faster and more safely. The position contributes to BusPatrol’s platform modernization and AI-native SDLC efforts by 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. The role is based in Austin, Texas, is full-time, and requires less than 5% travel. The position is not eligible for employment sponsorship.

Requirements

At least 5 years of experience in DevOps, Platform Engineering, SRE, or cloud infrastructure roles.
Bachelor’s degree in Computer Science, Engineering, Information Systems, or equivalent practical experience.
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, with depth in at least one area and 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.
Must be eligible to work without employment visa sponsorship; the position is not eligible for sponsorship or sponsorship transfer.

Nice To Haves

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.

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 reliability, deployment patterns, scaling, upgrades, and operational readiness.
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 through automation, observability, versioning, and rollback practices.
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 for 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 operationalizing spec-driven workflows, AI-assisted PR practices, automated test generation, and controlled release-readiness checks.
Contribute to secure, repeatable, observable AI/ML delivery systems aligned with 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 delivery system.
Collaborate 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 modernization, reliability, and AI adoption goals.