AI Infrastructure Engineer

About the Role

As an AI Infrastructure Engineer, you’ll build and maintain the systems that power our AI applications and model training. Your work will ensure our teams can run AI workloads reliably, securely, and at scale — across both cloud and on-prem environments.

This role combines DevOps, ML systems, and infrastructure engineering to support everything from LLMs to training pipelines.

What you’ll do:

• Build and maintain infrastructure for model hosting, prompt execution, and training workflows
• Operate and optimize model-serving systems like vLLM, Triton, or OpenAI proxies
• Implement secure API gateways and manage token usage, routing, and fallback
• Develop and support training pipelines, GPU scheduling, and experiment tracking
• Maintain CI/CD systems, observability tools, and infrastructure documentation

What We’re Looking For

• 6+ years' experience in infrastructure engineering, DevOps, or ML systems
• Strong command of Kubernetes, Terraform, and cloud-native architecture (AWS, Azure, GCP)
• Experience with containerization, CI/CD, and API security practices
• Prior exposure to model hosting or ML pipeline orchestration
• Understanding of networking concepts including VPNs, VNets, and hybrid connectivity.
• Familiarity with security best practices for cross-platform infrastructure.
• Experience with on-prem infrastructure including networking, storage hardware

Bonus points if you have:

• Experience with GPU resource orchestration or Kubeflow
• Familiarity with inference servers like vLLM, Triton, TGI, or TorchServe
• Understanding of cost telemetry and resource budgeting for model traffic
• Security mindset and experience with IAM, logging, and compliance
• Familiarity with compliance frameworks (SOC2, GDPR, HIPAA) and implementing controls.
• Background in database management across different platforms

If you’re looking to do meaningful work with people who care about how we get there — we’d love to meet you. Apply now!