Lead DevOps Engineer

Lead DevOps Engineer

Join our Applied Intelligence Personalization team. You'll build and maintain scalable, low-latency infrastructure that powers personalization and engagement across Paramount's streaming platforms. Our workloads include real-time machine-learning inference, event-driven messaging, and high-traffic backend services — so deep Kubernetes, CI/CD, and cloud-infrastructure expertise is the core of the role, with ML-serving experience as a solid plus. The ideal candidate has 4+ years of production DevOps / SRE experience, is fluent in Kubernetes and infrastructure as code, and cares deeply about reliability, latency, and developer velocity.

Your Day-to-Day

• Design, implement, and manage scalable and reliable Kubernetes-based infrastructure for personalization services.
• Build and own CI/CD pipelines that ship services (and ML models) to production safely — canary, rollback, progressive delivery.
• Stand up observability and monitoring with Prometheus, New Relic, OpenTelemetry, and Grafana; define SLIs / SLOs and drive error-budget discipline.
• Ensure high availability, security, and performance of production APIs and streaming data pipelines.
• Partner with application, data, and ML engineers to integrate their workloads smoothly into the platform.
• Implement autoscaling strategies (HPA, KEDA, traffic-driven) for variable, bursty traffic patterns.
• Manage Pub/Sub and event-driven architectures for real-time messaging, engagement analytics, and inter-service communication.
• Optimize hot-path services using Redis, Memcached, and other caching strategies.
• Debug and tackle production issues around latency, scaling, and reliability.

Key Projects

• Build and optimize real-time serving infrastructure for personalization and engagement (including ML-inference workloads).
• Develop scalable, secure CI/CD pipelines for deploying services and ML models.
• Implement log aggregation and monitoring solutions for platform-wide observability.
• Optimize Kubernetes-based serving for minimal latency and efficient resource utilization.
• Improve A/B testing infrastructure to track the impact of personalized experiences.
• Enhance streaming data pipelines to support real-time data consumers.

Basic Qualifications

• 4+ years of experience in DevOps, Site Reliability Engineering (SRE), or Cloud Infrastructure Engineering.
• Solid experience with Kubernetes and container orchestration.
• Hands-on experience with CI/CD tools such as GitHub Actions, Jenkins, and ArgoCD.
• Deep knowledge of Google Cloud Platform (GCP), AWS, or Azure.
• Expertise in infrastructure as code (IaC) using Terraform and Helm.
• Experience with message queues and event-driven architectures (Pub/Sub, Kafka, etc.).
• Proficiency in monitoring and logging solutions (New Relic, Prometheus, OpenTelemetry, etc.).
• Strong scripting skills in Python, Bash, or Go for automation.
• Track record of owning production reliability — SLIs / SLOs, incident response, postmortems.

Preferred / Additional Qualifications

• Experience deploying and operating ML models in production (TF Serving, Triton, TorchServe, Ray Serve).
• GPU / accelerator scheduling and node-pool management on Kubernetes.
• Knowledge with load balancing, API gateways, and caching strategies at scale.
• Experience with A/B testing frameworks and experimentation infrastructure.
• Experience optimizing low-latency microservices for personalization or recommendation workloads.
• Passion for building and maintaining high-performance infrastructure for real-time applications.