Devops Jobs 2026 (Now Hiring) – Smart Auto Apply

Our Team

The Gen AI Experience Engineering team is pioneering AI integration across Adobe Experience Cloud,operatingwith a startup mentality and a focus on rapid, iterative delivery of scalable solutions that empower both our customers and internal teams.

The Opportunity

Are you passionate about enabling world-class AI-powered experiences through robust infrastructure and automation?We'reseeking a Senior DevOps Engineer to architect, implement, andmaintainthe shared app infrastructure supporting our AI Assistant and related services across Adobe Experience Cloud. This role requires deep technicalexpertisein infrastructure automation, cloud-native systems, andMLOpsengineering, along with strong cross-functional collaboration skills. The position spans custom tool development, deployment, performance validation, and monitoring—ensuringhighly reliableand secure infrastructure to support Adobe's rapidly evolving AI initiatives.

Impact You'll Make

You'llbe instrumental in designing andoperatingthe backbone that powers our AI Assistant and shared applications. Your work will ensure reliability, scalability, and security for these critical services—enabling rapid experimentation, seamless model deployment, and the continuous delivery of new AI-powered features at scale.

Key Responsibilities

• Architect, build, andmaintainCI/CD pipelines for AI Assistant, ML services, and traditional app services, ensuring seamless integration of diverse software components across Experience Cloud

• Design and manage cloud infrastructure (Azure, AWS, GCP) for scalable, secure, and cost-effective AI/ML operations

• Create resilient automation pipelines, orchestrate Kubernetes-based environments, and deploy microservices using Helm

• Automate bare-metal-to-cloud provisioning, deployment, monitoring, and incident response for AI-powered applications

• Build monitoring systems and automated alerts using Datadog, Prometheus, or ELK/Kibana; diagnose and resolve complex build and infrastructure failures

• Integrate security and static code analysis tools into pipelines; ensure compliance with security and privacy standards across all environments

• Collaborate with engineering, product, and ML teams tooptimizeworkflows and infrastructure for rapid model iteration and deployment

• Implement infrastructure-as-code and configuration management best practices (Terraform, Ansible)

• Drive continuous improvement in reliability, performance, and developer productivity

• Play a critical role in scaling enterprise deployment workflows, refining automation architectures, and enabling rapid iteration across the Gen AI Experience Engineering organization

What You Need to Succeed

The three things that matter most for this role:

5+ years in DevOps, SRE, or Cloud Infrastructure— and the depth of knowledge that comes with it

Strong custom tool development skills— you build the automation others rely on (more than just scripting)

AI/ML infrastructure fluency— you understand what AI/ML systems need to run reliably, and you use AI/ML to make DevOps processes smarter

If those three describe you,here'swhat the day-to-day requires:

• Deepexpertisewith cloud platforms (Azure, AWS, GCP) and container orchestration (Kubernetes, Docker)

• Proven experience building andmaintainingCI/CD pipelines (GitHub Actions, Jenkins, Azure DevOps, etc.)

• Extensive Python automation skills and Kubernetes/Helm-based microservice deployment experience

• Strong scripting and custom tool development (Python, Bash, PowerShell) and Linux system administration

• Solid understanding of observability tools — Prometheus, Grafana, ELK/Kibana — and secure development best practices

• Experience supporting AI/ML workloads and data pipelines in production

• Solid understanding of security, compliance, and privacy in cloud environments

• Excellent collaboration and communication skills

• Startup mentality: you thrive in fast-paced, iterative environments

Nice to Have

• Experience with infrastructure for large-scale generative AI or LLM-based systems

• Knowledge of Adobe Experience Platform or similar enterprise SaaS ecosystems

• Contributions to open-source DevOps or cloud infrastructure projects