Job Overview
We are seeking an experienced Senior AI/ML Infrastructure Engineer to design, build, deploy, and operate scalable infrastructure supporting Generative AI and Predictive AI platforms.
The ideal candidate will have strong expertise in infrastructure engineering, Kubernetes, GPU computing, distributed systems, CI/CD automation, and Site Reliability Engineering (SRE).
This role involves supporting AI/ML model training, inference, and batch processing workloads while ensuring platform reliability, scalability, performance, and operational efficiency.
Key Responsibilities:
Infrastructure Engineering & AI/ML Platforms
* Lead complex infrastructure initiatives supporting Generative AI and Predictive AI platforms from design through production deployment.
* 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.
* Design and maintain reliable, scalable, and highly available infrastructure for AI/ML applications.
* Collaborate with AI/ML engineering, application development, infrastructure, and platform teams to support production workloads.
Kubernetes, OpenShift & GPU Infrastructure
* Deploy, configure, troubleshoot, and maintain Kubernetes and OpenShift clusters.
* Support GPU-enabled container workloads and distributed AI/ML training environments.
* Optimize GPU utilization, cluster capacity, resource allocation, and workload performance.
* Troubleshoot infrastructure, networking, storage, container orchestration, and GPU-related issues.
* Implement infrastructure automation and configuration management best practices.
Monitoring, Observability & Reliability
* Own monitoring, alerting, and observability using Grafana, Splunk, and enterprise telemetry tools.
* Define SLIs/SLOs and build actionable alerts to proactively detect performance, capacity, and resiliency risks.
* Monitor platform health, availability, latency, throughput, and infrastructure resource utilization.
* Implement dashboards and automated alerts for critical AI/ML infrastructure components.
* Improve platform reliability through proactive monitoring and performance optimization.
Automation, AIOps & Incident Management
* Build AI- and agent-based automation tools for self-healing, scaling, diagnostics, and incident remediation.
* Apply AIOps techniques to reduce alert fatigue and improve platform reliability.
* Lead production incident analysis and ensure operational rigor and root-cause prevention.
* Conduct root-cause analysis (RCA) for infrastructure failures and production incidents.
* Develop automation scripts and operational tools to reduce manual intervention.
* Establish incident response, problem management, and continuous improvement processes.
Technical Leadership & Collaboration
* Mentor engineers and influence stakeholders across a geographically distributed organization.
* Provide technical guidance on infrastructure architecture, troubleshooting, automation, and reliability.
* Collaborate with development, DevOps, SRE, security, and AI/ML engineering teams.
* Drive engineering best practices, operational standards, and continuous platform improvements.
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.
