ML Ops Engineer

MSH; Charlotte (NC); On-site; USD 83,000 - 152,000; Part time

Job summary

MSH is seeking an experienced MLOps Engineer to lead infrastructure for enterprise-scale Generative AI and Predictive AI platforms in Charlotte, NC. The role emphasizes GPU-accelerated environments, OpenShift, Kubernetes, automation, and observability at scale.

The ideal candidate will bring hands-on experience supporting production AI/ML workloads in regulated enterprise settings, with strong Linux administration, CI/CD, and incident-management skills.

Qualifications

• 5+ years of infrastructure engineering experience.
• 5+ years troubleshooting end-to-end architectures, including CI/CD pipelines.
• 5+ years Linux systems administration and troubleshooting.
• 4+ years supporting AI/ML platforms.
• 4+ years Kubernetes/container platforms production operations.
• Strong experience with automation, production support, and incident management.

Responsibilities

• Lead infrastructure initiatives supporting Generative AI and Predictive AI platforms from design through production operations.
• Design, deploy, and manage OpenShift-based container platforms optimized for GPU-accelerated workloads.
• Support AI/ML model training, inference, and batch processing environments.
• Build and operate scalable GPU infrastructure, including multi-node GPU clusters and GPU SuperPod architectures.
• Develop and maintain monitoring, alerting, and observability solutions using Grafana, Splunk, and enterprise telemetry tools.
• Define and implement SLIs/SLOs to proactively identify performance, capacity, and reliability risks.
• Develop AI-driven and agent-based automation for self-healing, scaling, diagnostics, and incident remediation.
• Apply AIOps practices to reduce alert fatigue and improve platform reliability.
• Lead production incident investigations, root-cause analysis, and preventive remediation initiatives.
• Collaborate with cross-functional and geographically distributed engineering teams while mentoring other engineers.
• Ensure platform stability, availability, resiliency, and compliance with enterprise operational controls.

Skills

MLOps

Kubernetes

OpenShift

CI/CD

Linux administration

Incident management

Tools

Grafana

Splunk

Telemetry pipelines

Job description

Employment Type: Contract

Experience: 5+ Years

About the Role

We are seeking an experienced MLOps Engineer to lead the infrastructure supporting enterprise-scale Generative AI and Predictive AI platforms. This role focuses on designing, deploying, and operating high-performance, GPU-accelerated environments, with an emphasis on OpenShift, Kubernetes, automation, and observability.

The ideal candidate will bring strong infrastructure engineering expertise and hands-on experience supporting production AI/ML workloads in complex, highly regulated enterprise environments.

Key Responsibilities

• Lead infrastructure initiatives supporting Generative AI and Predictive AI platforms from design through production operations.
• Design, deploy, and manage OpenShift-based container platforms optimized for GPU-accelerated workloads.
• Support AI/ML model training, inference, and batch processing environments.
• Build and operate scalable GPU infrastructure, including multi-node GPU clusters and GPU SuperPod architectures.
• Develop and maintain monitoring, alerting, and observability solutions using Grafana, Splunk, and enterprise telemetry tools.
• Define and implement SLIs/SLOs to proactively identify performance, capacity, and reliability risks.
• Develop AI-driven and agent-based automation for self-healing, scaling, diagnostics, and incident remediation.
• Apply AIOps practices to reduce alert fatigue and improve platform reliability.
• Lead production incident investigations, root-cause analysis, and preventive remediation initiatives.
• Collaborate with cross-functional and geographically distributed engineering teams while mentoring other engineers.
• Ensure platform stability, availability, resiliency, and compliance with enterprise operational controls.

Required Qualifications

• 5+ years of infrastructure engineering experience.
• 5+ years of experience troubleshooting complex end-to-end architectures, including CI/CD pipelines.
• 5+ years of hands-on Linux systems administration and troubleshooting experience.
• 4+ years of experience supporting AI/ML platforms.
• 4+ years of experience with Kubernetes and container platforms, including production operations.
• Strong experience with infrastructure automation, production support, and incident management.

Preferred Qualifications

• Experience supporting Generative AI and Predictive AI platforms.
• Hands-on experience operating GPU infrastructure, including scheduling, quotas, resource allocation, and performance tuning.
• Experience with OpenShift in GPU-enabled, multi-tenant environments.
• Experience designing, deploying, or operating GPU SuperPods and large-scale GPU clusters.
• Strong observability experience with Grafana, Splunk, and custom telemetry pipelines.
• Experience supporting AI/ML workloads on Google Cloud Platform (GCP) and Microsoft Azure, including GPU-backed services and managed AI infrastructure.
• Experience managing hybrid-cloud or multi-cloud AI platforms, including networking, identity, cloud-native services, and cost optimization.
• Knowledge of AI/ML operational metrics, including inference latency, GPU utilization, and workload performance.
• Experience with Business Continuity Planning (BCP), Disaster Recovery (DR), high availability, and resilient architecture design.

Job Expectations

• Participate in a 24x7 on-call rotation.
• Take ownership of production stability, platform health, and service reliability.
• Operate effectively in a highly regulated enterprise environment with strong risk management and compliance requirements.

Interested?

If you have strong experience in MLOps, Kubernetes, OpenShift, GPU infrastructure, and production AI/ML platforms, we'd love to hear from you!