DevOps Engineer

Full-time - Mid Level

NY

About the position

We are looking for a DevOps Engineer - Personalization Services to join our Applied Intelligence Personalization Team. This role will focus on infrastructure support for ML applications running on Kubernetes and Ray Clusters, ensuring high-performance computing for content recommendations and personalization services. The ideal candidate will have 2+ years of experience working with Kubernetes, TensorFlow (TF), Prometheus, and high-performance parallel computing for scaling ML workloads efficiently.

Responsibilities

• Design, implement, and manage scalable and reliable infrastructure for ML-based personalization services.
• Optimize Kubernetes-based deployments for ML training and inference workloads.
• Manage and scale Ray Clusters for distributed ML workloads and parallel computing.
• Automate CI/CD pipelines to streamline the deployment of ML models and services.
• Develop observability and monitoring solutions using tools like Prometheus, Datadog, and OpenTelemetry.
• Ensure high availability, security, and performance of ML infrastructure.
• Work with ML engineers and backend teams to integrate scalable model training and inference systems.
• Implement autoscaling strategies for Ray-based workloads based on computational demand.
• Optimize ML infrastructure for TensorFlow model training and serving.
• Debug and resolve production issues related to latency, scaling, and reliability.

Requirements

• 2+ years of experience in DevOps, Site Reliability Engineering (SRE), or Cloud Infrastructure Engineering as well as strong knowledge of GCP, AWS, or AZURE.
• Hands-on experience with online inferencing, expertise in TensorFlow model training and serving, and experience with high-performance parallel computing architectures.
• Hands-on experience with CI/CD tools such as GitHub Actions, Jenkins, or GitLab CI, strong experience with Kubernetes and container orchestration, as well as expertise in infrastructure as code (IaC) using Terraform or Helm.

Nice-to-haves

• Experience with message queues and event-driven architectures (Pub/Sub, Kafka, etc.).
• Proficiency in monitoring and logging solutions (Datadog, Prometheus, OpenTelemetry, etc.).
• Strong scripting skills in Python, Bash, or Go for automation.
• Hands-on experience with ML model serving frameworks (TensorFlow Serving, Triton, TorchServe, etc.).
• Familiarity with load balancing, API gateways, and caching strategies.
• Experience optimizing low-latency microservices for ML-based personalization.
• Familiarity with Argo CD.
• Passion for building and maintaining high-performance infrastructure for large-scale ML applications.

Benefits

• A culture of learning passionate about innovative ML infrastructure and DevOps standard processes.
• A collaborative team environment where engineering supports real-time personalization.
• A remote-friendly work setup with opportunities to work on scalable ML training and inference systems.
• Medical, dental, vision, 401(k) plan, life insurance coverage, disability benefits, tuition assistance program and PTO.
• This position is bonus eligible.