As an
AI Infrastructure Engineer IV, you will play a critical role in designing, building, and maintaining the systems that power our AI and machine learning capabilities. You will ensure our compute, storage, and cloud environments are scalable, efficient, and tuned for high-performance AI workloads. Working closely with data scientists, robotics engineers, and software teams, you'll develop robust infrastructure that supports the deployment and reliability of our AI-driven autonomous systems.
Responsibilities:- Design, build, and maintain high-performance computing infrastructure including CPUs, GPUs, storage, and networking to support AI and ML workloads.
- Deploy and manage AI systems within cloud environments (AWS, Azure, GCP), ensuring scalability, cost-efficiency, and high availability.
- Collaborate with data scientists, ML engineers, and software teams to support AI model development, training, and deployment workflows.
- Implement automation, CI/CD, DevOps, and MLOps practices to create efficient, repeatable, and reliable AI infrastructure processes.
- Optimize compute and storage systems to achieve maximum performance and throughput for AI/ML pipelines.
- Monitor system health and troubleshoot performance bottlenecks, infrastructure issues, and deployment challenges.
Required Qualifications:- Bachelor's degree in Computer Science, Computer Engineering, or a related technical field.
- 8+ years of experience in cloud infrastructure, DevOps, or platform engineering with 3+ years working on AI/ML systems.
- Strong understanding of modern AI infrastructure components, including distributed computing, GPU-accelerated systems, and large-scale storage.
- Hands-on experience with cloud platforms such as AWS, Azure, or Google Cloud.
- Proficiency with Kubernetes, Docker, Terraform, or similar containerization and orchestration tools.
- Strong programming skills in Python and/or C++, with experience supporting machine learning frameworks (TensorFlow, PyTorch, etc.).
- Experience implementing CI/CD pipelines, MLOps practices, and automation tooling.
