Devops Platform Engineer

Advanced Micro Devices, Inc•San Jose, CA

About The Position

ADVANCE YOUR CAREER. ADVANCE THE WORLD. At AMD, we believe technology has the power to solve the world’s most important challenges. From advancing healthcare and scientific discovery to powering AI and the technologies people rely on every day, innovation at AMD is shaping the future. Whether you’re designing next-gen processors, enabling AI breakthroughs, or bringing leading edge products to market, every role at AMD contributes to something bigger — technology that moves the world forward. Join us and, together, we’ll advance your career. THE ROLE: We are seeking a hands-on Platform Engineer to build and operate the infrastructure foundations that enable engineering teams to deliver reliable, scalable, and secure products. This role will shape the internal developer platform across Kubernetes, observability, AI-enabled workloads, CI/CD, and infrastructure as code. You will work closely with software, DevOps, security, and AI/ML teams to turn platform capabilities into a simple and dependable developer experience. The ideal candidate is comfortable moving between architecture decisions, production operations, automation, and practical enablement of partner teams.

Requirements

Bachelor’s degree in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience.
6+ years of experience in software engineering, DevOps, SRE, cloud infrastructure, or platform engineering.
Strong hands-on experience operating and automating Kubernetes environments.
Experience with container technologies and deployment tooling, such as Docker, Helm, Kustomize, Argo CD, Flux, or similar.
Experience implementing infrastructure as code using Terraform, Pulumi, CloudFormation, Ansible, or comparable tools.
Practical experience with observability platforms and concepts, including metrics, logging, distributed tracing, alerting, dashboards, and SLOs.
Proficiency in at least one programming or scripting language such as Python, Go, Bash, or TypeScript.
Experience with CI/CD systems and automated software delivery practices.
Strong troubleshooting skills across Linux, networking, containers, cloud or on-premise infrastructure, and distributed systems.
Clear written and verbal communication skills, with an ability to work effectively across engineering disciplines.

Nice To Haves

Experience designing internal developer platforms or large-scale shared infrastructure.
Experience with Prometheus, Python, Bash, Grafana, OpenTelemetry, Loki, Tempo, Elastic, Datadog, Splunk, or comparable observability systems.
Experience with IBM LSF and Netapp Storage
Experience with GitOps operating models.
Experience supporting AI/ML infrastructure, model serving, GPU scheduling, Kubernetes operators, or AI workload orchestration.
Knowledge of platform security practices, including identity and access management, secrets management, policy enforcement, image security, and supply-chain security.
Experience with service mesh, API gateways, ingress, networking, or multi-cluster Kubernetes architectures.
Demonstrated ownership of production systems and a bias toward automation, operational excellence, and continuous improvement.
Semiconductor experience very helpful, EDA experience a plus.

Responsibilities

Design, build, and operate scalable Kubernetes-based platform capabilities for development, test, and production workloads.
Create reusable infrastructure-as-code modules, patterns, and automated workflows using tools such as Terraform, Ansible, Helm, Kustomize, or equivalent technologies.
Develop and evolve platform observability: metrics, logs, traces, dashboards, alerting, service-level objectives, and operational runbooks.
Improve platform reliability, capacity management, security posture, and cost efficiency through automation and data-driven operational practices.
Build the infrastructure required to deploy, operate, observe, and govern AI/ML workloads, including model-serving, GPU-enabled compute, data-access, and workload-isolation patterns where applicable.
Enable self-service for product and engineering teams through well-designed platform APIs, templates, documentation, golden paths, and CI/CD integrations.
Partner with application, security, infrastructure, and AI teams to define platform standards and remove delivery bottlenecks.
Investigate complex production issues, lead root-cause analysis, and implement durable preventative improvements.
Contribute to technical roadmaps, architecture reviews, and engineering standards for the platform.