Job Description: Senior AI/ML Infrastructure Engineer – GenAI Platform / SRE
Location: Charlotte, NC
Required Qualifications
* 5+ years of infrastructure engineering experience.
* 5+ years troubleshooting complex end-to-end architectures, including CI/CD pipelines.
* 5+ years of Linux system administration experience.
* 4+ years supporting AI/ML platforms.
* 4+ years of Kubernetes/container platform experience, including production support.
* Strong hands-on experience with Kubernetes, OpenShift, and containerized workloads.
* Experience supporting GPU-based computing infrastructure and distributed systems.
* Experience with monitoring and observability tools such as Grafana and Splunk.
* Strong understanding of SRE principles, SLIs, SLOs, incident management, and root-cause analysis.
* Experience with CI/CD pipelines, infrastructure automation, and production troubleshooting.
* Strong analytical, problem-solving, communication, and technical leadership skills.
Desired Qualifications
The screenshot cuts off most of this section. The following qualifications are suggested additions consistent with the visible responsibilities.
* Experience with Generative AI and Predictive AI platforms.
* Experience managing large-scale GPU clusters or GPU SuperPod architectures.
* Familiarity with NVIDIA GPU technologies, CUDA, and GPU workload scheduling.
* Experience supporting AI/ML frameworks such as PyTorch and TensorFlow.
* Knowledge of model training, inference pipelines, and distributed AI workloads.
* Experience with Infrastructure as Code (IaC) tools such as Terraform or Ansible.
* Experience developing automation using Python, Bash, or PowerShell.
* Familiarity with AIOps, intelligent alerting, automated diagnostics, and self-healing infrastructure.
* Experience with cloud platforms such as Azure, AWS, or Google Cloud.
* Understanding of enterprise networking, storage, security, and high-availability architectures.
Key Technical Skills
Category Technologies / Skills
AI/ML Infrastructure Generative AI, Predictive AI, Model Training, Inference
Container Platforms Kubernetes, OpenShift, Docker
GPU Infrastructure GPU Clusters, GPU SuperPod, Distributed Computing
Operating Systems Linux Administration
DevOps & Automation CI/CD, Infrastructure as Code, Python, Bash
Monitoring Grafana, Splunk, Enterprise Telemetry
Reliability Engineering SRE, SLI, SLO, RCA, Incident Management
AIOps Self-Healing, Automated Diagnostics, Intelligent Alerting
Cloud Platforms Azure, AWS, Google Cloud Platform (preferred)
Leadership Technical Mentoring, Cross-functional Collaboration
Ideal Candidate Profile
The ideal candidate is a hands-on infrastructure engineer with extensive experience managing production AI/ML platforms, Kubernetes/OpenShift environments, and GPU computing infrastructure.
They should be capable of independently troubleshooting complex infrastructure issues, automating operational processes, implementing observability solutions, and improving platform reliability.
Experience working with large-scale enterprise AI infrastructure and leading technical initiatives across distributed engineering teams will be highly valuable.
