Sr. Cloud Infrastructure & DevOps Engineer

AI Vision Processors For Edge Applications

Our solutions make cameras smarter by extracting valuable data from high-resolution video streams.

Job Description

We are looking for a hands-on Cloud Infrastructure & DevOps Engineer to design, implement, and operate scalable cloud infrastructure, CI/CD systems, developer platforms, and observability solutions. The ideal candidate has deep experience with Kubernetes, cloud-native technologies, infrastructure-as-code, automation, and modern software delivery practices.

This role will work closely with software, AI, SDK, and product engineering teams to enable rapid development, testing, deployment, and operation of Ambarella's cloud platforms, developer environments, and AI infrastructure.

Responsibilities

Cloud Infrastructure

·                Design, deploy, and maintain cloud infrastructure across public cloud environments (GCP preferred, AWS and Azure experience valued).

• Build secure, scalable, highly available environments using Infrastructure as Code.
• Manage networking, identity, access management, secrets, and security controls.
• Optimize cloud architecture for performance, reliability, scalability, and cost.

Kubernetes & Platform Engineering

·                Deploy and operate Kubernetes clusters and containerized workloads.

• Develop multi-tenant developer environments and internal platform services.
• Manage container registries, artifact repositories, and software distribution infrastructure.
• Implement platform automation, lifecycle management, and self-service engineering capabilities.

CI/CD & Developer Productivity

·                Build and maintain automated build, test, release, and deployment pipelines.

• Improve software delivery velocity through automation and standardization.
• Support build systems, source control integrations, and deployment workflows.
• Partner with engineering teams to streamline development processes and improve DevEx.

Reliability & Operations

·                Implement monitoring, logging, alerting, and tracing solutions.

• Define SLOs, SLIs, and operational best practices.
• Lead incident response, root cause analysis, and remediation efforts.
• Improve system resilience, uptime, and operational maturity.

Security & Compliance

·                Implement security best practices across cloud platforms and CI/CD workflows.

• Manage secrets, certificates, vulnerability scanning, and policy enforcement.
• Support compliance, audit, and governance requirements.
• Collaborate with security stakeholders to maintain secure development and operational environments.

AI & Edge Infrastructure Enablement

·                Support cloud infrastructure used for AI model development, testing, and deployment.

• Enable scalable infrastructure for simulation, remote hardware access, and edge AI workflows.
• Contribute to infrastructure supporting developer portals, cloud IDEs, SDK delivery, and AI development environments.

Required Qualifications

·                BS/MS in Computer Science, Computer Engineering, or related field.

• 5+ years of experience in DevOps, Site Reliability Engineering, Platform Engineering, or Cloud Infrastructure.
• Strong experience with Kubernetes in production environments.
• Experience with Docker and containerized application deployment.
• Strong scripting and automation skills in Python, Bash, or Go.
• Experience with Infrastructure as Code tools such as Terraform.
• Experience building and operating CI/CD platforms.
• Knowledge of cloud networking, IAM, monitoring, logging, and security best practices.
• Experience with Linux systems administration.
• Familiarity with Git-based development workflows.

Preferred Qualifications

·                Experience with Google Cloud Platform.

• Experience operating GPU-based infrastructure for AI/ML workloads.
• Experience with ArgoCD, Flux, Helm, or GitOps workflows.
• Experience with Prometheus, Grafana, ELK/OpenSearch, Datadog, or similar observability platforms.
• Experience supporting developer platforms and internal engineering tools.
• Familiarity with AI/ML pipelines and MLOps practices.
• Experience with semiconductor, embedded systems, edge AI, robotics, or vision-based applications.

Key Technologies

·                Google Cloud Platform (GKE, Cloud Build, Artifact Registry, Pub/Sub, Cloud Functions, IAM)

• Kubernetes
• Docker
• Terraform
• GitHub / GitLab
• CI/CD Pipelines
• Prometheus / Grafana
• Python, Bash, Go
• Linux
• Monitoring and Observability Platforms
• Security and Identity Management Solutions 

This is a temporary assignment of 3-6 months.  The hourly rate is $69-$84 an hour.