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We are looking for a highly skilled Senior AI Infrastructure Engineer to design, build, operate, and optimize scalable infrastructure that supports enterprise AI/ML platforms and workloads. The ideal candidate will have deep expertise in cloud-native technologies, Kubernetes, observability, automation, and platform reliability, with a strong focus on enabling AI/ML teams through robust and efficient infrastructure.
Responsibilities
- Design, build, and operate scalable infrastructure supporting enterprise AI/ML platforms and workloads
- Manage and maintain Kubernetes and Docker environments for containerized applications
- Operate and optimize infrastructure on cloud platforms to support AI/ML workloads
- Develop automation scripts and tools to streamline platform operations
- Implement and maintain monitoring, logging, and observability solutions across infrastructure
- Administer Linux systems and ensure reliable platform operations
- Apply system design principles to improve scalability, performance, and reliability
- Troubleshoot infrastructure, platform, application, and container orchestration issues
- Conduct log analysis, root cause analysis, and incident management for production issues
- Configure and maintain networking components, including DNS, load balancing, ingress, and security controls
- Provide operational support for mission-critical production environments
Requirements
- Bachelor's degree in Engineering (B.Tech/B.E.) with 5+ years of relevant experience
- Expertise in Kubernetes and Docker for container orchestration and management
- Experience managing infrastructure on at least one major cloud platform (Azure, AWS, or GCP)
- Proficiency in scripting and automation using Python, Shell, or Bash
- Familiarity with monitoring, logging, and observability tools such as Prometheus, Grafana, and ELK/OpenSearch or Datadog
- Understanding of Linux systems administration and platform operations
- Knowledge of system design, scalability, performance optimization, and reliability engineering
- Skills in troubleshooting infrastructure, platform, application, and container orchestration issues
- Capability to perform log analysis, root cause analysis, and incident management
- Understanding of networking fundamentals, including DNS, load balancing, ingress, and security concepts
- Background in providing operational support for mission-critical production environments
Nice to have
- Background in supporting AI/ML platforms or GPU-based workloads
- Knowledge of advanced networking concepts such as service mesh (Istio/Linkerd), network policies, API gateways, and traffic management
- Familiarity with cloud-native architecture patterns and platform engineering best practices
- Familiarity with SRE practices, including SLIs, SLOs, error budgets, and disaster recovery
- Hands-on experience with Infrastructure as Code tools such as Terraform and Helm Charts
- Experience in cloud cost monitoring and optimization for high-compute AI workloads