Kubernetes AI Engineer
Insight Global
Job Description Insight Global is seeking a Kubernetes AI Engineer for a leading enterprise client in the technology space. This role will focus on designing, building, and optimizing scalable AI platform infrastructure that supports end-to-end machine learning workflows, including model development, training, and inference. The ideal candidate brings deep Kubernetes and containerization expertise, along with a strong foundation in Linux systems and platform engineering. They will play a key role in driving performance, security, automation, and observability across enterprise AI environments, while partnering closely with cross-functional teams to deliver resilient, compliant, and high-performing platform solutions. Day-to-Day:
- Design and engineer Kubernetes-based AI platform infrastructure supporting model development, training, and inference
- Build and manage containerized environments using Docker, Kubernetes, Operators, and Helm
- Optimize cluster performance, workload placement, scalability, and resource utilization
- Implement enterprise-grade security and compliance controls across Kubernetes environments
- Develop and maintain observability frameworks (monitoring, logging, alerting, dashboards)
- Automate deployment, configuration, patching, and platform operations using scripting and IaC
- Collaborate with AI/ML engineers, data scientists, DevOps, and infrastructure teams to deliver scalable solutions
- Translate business and technical requirements into platform architecture
- Create and maintain architecture documentation, runbooks, and operational standards
- Troubleshoot and resolve platform issues in complex enterprise environments
- 5+ years of experience in Kubernetes, container platform engineering, Linux systems, or AI/ML infrastructure in enterprise environments
- Strong expertise in Kubernetes architecture, cluster deployment, orchestration, and lifecycle management
- Hands-on experience with Docker, container runtimes, Operators, and Helm charts
- Experience designing and engineering Kubernetes-based AI platform infrastructure for model development, training, and inference
- Strong Linux administration and troubleshooting skills across compute, networking, and storage layers
- Proficiency in Python, shell scripting, YAML, and JSON for automation and platform engineering
- Experience implementing security controls (RBAC, network policies, pod security, secrets management)
- Experience with observability and monitoring tools such as Prometheus, Grafana, and logging frameworks
- Experience optimizing clusters for performance, scalability, resiliency, and resource utilization
- Familiarity with CI/CD, GitOps, infrastructure automation, and environment standardization practices
- Experience supporting AI/ML platforms, model deployment pipelines, or GPU-enabled workloads
- Strong problem-solving and analytical skills in complex environments
- Clear written and verbal communication skills
- Strong collaboration skills across cross-functional teams (AI/ML, DevOps, Infrastructure)
- Strong documentation discipline and operational rigor
- Self-starter mindset with accountability and continuous improvement focus • Kubernetes certifications (CKA, CKAD)
- Understanding of vulnerability management, enterprise controls, and remediation practices
- Experience building agentic AI solutions in enterprise environments
- Familiarity with Copilot Studio for app development
- Experience with AI/ML frameworks and model-serving platforms (PyTorch, TensorFlow, Triton, vLLM)
- Familiarity with GPU platforms and NVIDIA software stack
- Experience with OpenShift, hybrid cloud, or enterprise platform modernization initiatives Insight Global
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