MLOPS Engineer

Lead complex infrastructure initiatives supporting Generative AI and Predictive AI platforms from design to production operations. • Serve as a technical lead for platforms supporting AI/ML model training, inference, and batch workloads. • Design, build, deploy, and operate OpenShift-based container platforms optimized for high-performance GPU workloads. • Build, support and operate scalable GPU SuperPod architecture with large multi-node GPU clusters. • Own monitoring, ing, and observability using Grafana, Splunk, and enterprise telemetry tools. • Define SLIs/SLOs and build actionable s to proactively detect performance, capacity, and resiliency risks. • Build AI- and agent-based automation tools for self-healing, scaling, diagnostics, and incident remediation. • Apply AIOps techniques to reduce fatigue and improve platform reliability. • Lead production incident analysis and ensure operational rigor and root-cause prevention. • Mentor engineers and influence stakeholders across a geographically distributed organization. Required Qualifications • 5+ years of infrastructure engineering experience. • 5+ years troubleshooting complex end-to-end architectures (including CI/CD pipeline). • 5+ years Linux systems experience. • 4+ years supporting AI/ML platforms. • 4+ years of Kubernetes / container platform experience including production support. Desired Qualifications • Experience with Generative AI and Predictive AI platforms. • Hands-on GPU platform operations including scheduling, quota, and performance tuning. • Experience with OpenShift in GPU-enabled, multi-tenant environments. • Experience designing or operating GPU Super Pods. • Deep experience with observability using Grafana, Splunk, and custom telemetry pipelines. • Experience building AI- or agent-driven automation tooling (AIOps). • Hands-on experience supporting AI/ML workloads on GCP and Azure, including GPU-backed services and managed AI infrastructure • Experience operating hybrid or multi-cloud AI platforms, with an understanding of cloud-native services, networking, identity, and cost optimization for Generative and Predictive AI • Strong monitoring of AI signals such as inference latency and GPU utilization. • Experience with BCP/DR, resiliency, and highly available architectures. Job Expectations • Participation in a 24x7 on-call rotation. • Ownership for production stability, platform health, and customer outcomes. • Operate in regulated enterprise environments with strong risk and control focus.