Lead DevOps Engineer

Paramount•New York, NY

8d•Onsite

About The Position

We are looking for a Lead DevOps Engineer to 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.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.