MLOps Engineer

Requirements:

• Were looking for 5+ years of infrastructure engineering experience.
• We require 5+ years of experience troubleshooting complex end-to-end architectures, including CI/CD pipelines.
• We require 5+ years of Linux systems experience.
• We require 4+ years supporting AI/ML platforms.
• We require 4+ years of Kubernetes or container platform experience, including production support.
• We prefer strong experience with MLOps, CI/CD pipelines, cloud platforms, model deployment, monitoring, and automation.
• We value experience with Generative AI and Predictive AI platforms.
• We value hands-on GPU platform operations, including scheduling, quota management, and performance tuning.
• We value experience with OpenShift in GPU-enabled, multi-tenant environments and with designing or operating GPU SuperPods.
• We value deep observability experience with Grafana, Splunk, and custom telemetry pipelines.
• We value experience building AI- or agent-driven automation tools and applying AIOps techniques.
• We value hands-on experience supporting AI/ML workloads on GCP and Azure, including GPU-backed services and managed AI infrastructure.
• We value experience operating hybrid or multi-cloud AI platforms and understanding cloud-native services, networking, identity, and cost optimization for Generative and Predictive AI.
• We value experience monitoring AI signals such as inference latency and GPU utilization.
• We value experience with business continuity and disaster recovery, resiliency, and highly available architectures.

Responsibilities:

• We need you to lead complex infrastructure initiatives for Generative AI and Predictive AI platforms, from design through production operations.
• We need you to serve as technical lead for platforms supporting AI/ML model training, inference, and batch workloads.
• We need you to design, build, deploy, and operate OpenShift container platforms optimized for high-performance GPU workloads.
• We need you to build, support, and operate scalable GPU SuperPod architecture with large multi-node GPU clusters.
• We need you to own monitoring and observability using Grafana, Splunk, and enterprise telemetry tools.
• We need you to define SLIs and SLOs and develop actionable measures to proactively identify performance, capacity, and resiliency risks.
• We need you to build AI- and agent-based automation tools for self-healing, scaling, diagnostics, and incident remediation.
• We need you to apply AIOps techniques to reduce operational fatigue and improve platform reliability.
• We need you to lead production incident analysis and maintain operational rigor, preventing recurring root causes.
• We need you to mentor engineers and influence stakeholders across a geographically distributed organization.
• We expect you to take part in a 24x7 on-call rotation and own production stability, platform health, and customer outcomes.

Technologies:

• AI
• Azure
• CI/CD
• Cloud
• GCP
• Grafana
• Support
• Kubernetes
• Linux
• MLOps
• Model Training
• OpenShift
• Splunk
• NodeJS

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Were hiring an MLOps Engineer for a role with location preferences in Charlotte, North Carolina, or in Texas, Arizona, or New Jersey. Youll work on AI infrastructure in regulated enterprise environments, where risk and controls are a strong focus.

last updated 43 week of 2026