Cisco AI Infrastructure Engineer 

India, Karnataka, Bengaluru 

245047569

We thrive in a fast-paced, experimentation-rich environment where new technologies aren’t just welcome — they’re expected. Here, you'll work side-by-side with seasoned engineers, architects, and thinkers to craft the kind of iconic products that can reshape industries and unlock entirely new models of operation for the enterprise.

If you're energized by the challenge of solving hard problems, love working at the edge of what's possible, and want to help shape the future of AI infrastructure — we'd love to meet you.

- The performance and efficiency of AI workloads on the node.

- Advancements in AI and machine learning infrastructure, enabling better utilization and improving efficiency for applications across industries.

Key Responsibilities

- Design and develop node-level infrastructure components to support high-performance AI workloads.

- Benchmark, analyze, and optimize the performance of AI infrastructure, including CUDA kernels and memory management for GPUs.

- Minimize downtime through seamless config and upgrade architecture for software components.

- Manage the installation and deployment of AI infrastructure on Kubernetes clusters, including the use of CRDs and operators.

- Work with distributed system fundamentals to ensure scalability, resilience, and reliability.

Minimum Qualifications:

- Proficiency in programming languages such as Rust, C/C++, Golang, Python, or eBPF.

- Strong understanding of Linux operating systems, including user space and kernel-level components.

- Experience with Linux user space development, including packaging, logging, telemetry and lifecycle management of processes.

- Strong understanding of Kubernetes (K8s) and related technologies, such as custom resource definitions (CRDs).

- Strong debugging and problem-solving skills for complex system-level issues.

- Bachelor’s degree+ and relevant 5+ years of Engineering work experience.

Preferred Qualifications:

- Linux kernel and device driver hands-on expertise is a plus.

- Experience in GPU programming and optimization, including CUDA, UCX is a plus.

- Experience with high-speed data transfer technologies such as RDMA.

- Use of Nvidia GPU operators and Nvidia container toolkit and Nsight, CUPTI.

- Nvidia MIG and MPS concepts for managing GPU consumption.