Design, implement, and manage scalable and reliable infrastructure for online inference services
Optimize Kubernetes-based deployments for low-latency model serving and real-time personalization
Automate CI/CD pipelines to streamline the deployment of ML models and services
Develop observability and monitoring solutions using tools like Prometheus, New Relic, and OpenTelemetry
Ensure high availability, security, and performance of real-time inference APIs
Work with ML engineers and backend teams to integrate inference models efficiently into production
Implement autoscaling strategies for inference workloads based on traffic patterns and model demand
Manage Pub/Sub and event-driven architectures to enable real-time messaging and engagement analytics
Optimize model-serving infrastructure using Redis, Memcached, and other caching strategies
Debug and tackle production issues related to latency, scaling, and reliability

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
Experience working with real-time inference and ML model deployment
Deep knowledge of Google Cloud Platform (GCP), AWS, or Azure
Expertise in infrastructure as code (IaC) using Terraform or Helm
Experience with message queues and event-driven architectures (Pub/Sub, Kafka, etc.)
Proficiency in monitoring and logging solutions (New Relic, Prometheus, OpenTelemetry, etc.)
Deep scripting skills in Python, Bash, or Go for automation

Lead DevOps Engineer

Key skills