Jobs → Engineering → MLOps Engineer

About the Role We are seeking an MLOps Engineer to join our team in a hybrid role. This position involves designing and supporting AI infrastructure and managing container platforms for GPU-intensive workloads. Key Responsibilities • Design and support Generative AI and Predictive AI infrastructure from development to production • Build and operate Kubernetes and OpenShift container platforms for GPU-intensive AI/ML workloads • Support large-scale GPU clusters and SuperPod architectures • Manage CI/CD pipelines, Linux systems, model deployment, monitoring, and automation • Implement observability using Grafana, Splunk, and enterprise telemetry tools • Define SLIs/SLOs and improve platform reliability, performance, and resiliency • Develop AI-driven automation and AIOps solutions for self-healing, scaling, and incident remediation • Support production incidents, root-cause analysis, and disaster recovery Requirements • Strong MLOps and AI/ML platform engineering experience • Kubernetes, OpenShift, and container platform operations • Linux administration and troubleshooting • CI/CD pipeline development and support • GPU infrastructure, workload scheduling, and performance tuning • Monitoring and observability using Grafana and Splunk • Cloud platforms: GCP and Azure • Experience with production AI/ML workloads, automation, and high-availability architectures