AI Infrastructure Engineer III

We are looking for a highly motivated AI Infrastructure Engineer III to join our Cloud Engineering team. The ideal candidate is passionate about building scalable AI infrastructure that enables machine learning engineers and data scientists to efficiently develop, train, deploy, and operate AI models.

This role focuses on designing, operating, automating, and continuously improving AI platform capabilities including Kubeflow, MLflow, GPU infrastructure, distributed training platforms, model serving infrastructure, and MLOps tooling across cloud-native and hybrid environments.

The ideal candidate possesses deep expertise in AI infrastructure and GPU platforms. Kubernetes and cloud platform experience are essential enablers, but the primary focus is building highly optimized infrastructure for AI and machine learning workloads.

AI Platform Engineering

- Design, deploy, and operate enterprise AI/ML platforms.

- Build self-service platforms for Data Scientists and ML Engineers.

- Deploy and operate Kubeflow, MLflow, KServe, Ray, or similar AI platforms.

- Design infrastructure supporting model training, experimentation, feature engineering, and inference.

- Build highly available and scalable model serving infrastructure.

GPU Infrastructure

- Design and operate GPU clusters for large-scale AI workloads.

- Optimize GPU scheduling, utilization, sharing, autoscaling, and resource allocation.

- Deploy and manage NVIDIA GPU Operator and GPU-enabled Kubernetes environments.

- Optimize distributed GPU training performance across multi-node clusters.

- Troubleshoot AI infrastructure performance bottlenecks.

MLOps & Platform Automation

- Build CI/CD pipelines for ML workloads.

- Automate AI infrastructure provisioning using Infrastructure as Code.

- Implement monitoring and observability for GPU utilization, model serving, training jobs, and inference latency.

- Collaborate closely with Data Science teams to improve platform usability, performance, and reliability.