Meet the Team
As a Technical Lead, you will drive technical excellence across HPC infrastructure, network automation, DevOps practices, and SRE principles while leading architecture decisions and guiding teams in implementing high-performance solutions for AI/ML workloads on various network topology. This role combines deep technical expertise with leadership responsibilities, focusing on system architecture, automation, reliability engineering, and development excellence.
Your Impact
Design and implement end-to-end automation solutions for HPC infrastructure (Compute, network and storage) using Kubernetes operators, Terraform, and Ansible Analyzing compute, storage, network traffic patterns during distributed training/inference operations across different AI/ML frameworks Monitoring and optimizing network utilization patterns for various model architectures Identifying bottlenecks in network communication patterns Root cause analysis across network, compute, and storage layers. Experience with various failure scenarios and recovery procedures Architectural decision-making and innovation Develop infrastructure patterns for different workload type Benchmarking and Performance engineering leadership Mentor junior engineers through architecture reviews and code critiques Design and implement comprehensive telemetry collection systems for monitoring high-speed network microburst behavior. Develop sophisticated visualization tools and analytics frameworks to enable real-time identification of performance bottlenecks and system constraints, facilitating rapid optimization and troubleshooting.Minimum Qualifications (Provide up to five (5) bullet points - these should be clearly defined, objective, non-comparative, relevant, and quantifiable to appropriately evaluate applicants – not including soft skills):
Demonstrated expertise in distributed systems and infrastructure design (compute, storage, and networking) Experience with network automation tools and configuration management (Ansible, Python, golang, YAML, YANG) Strong background in CI/CD, gitops or similar practices and tools Expert-level experience with observability platforms and practices Strong background in implementing distributed tracing, metrics collection, and log aggregation systems Demonstrated experience in at least 1 completed performance benchmarking projects for distributed systems, storage, network, compute
Preferred Qualifications (Provide up to five (5) bullet points:
· Bachelor’s degree in computer science, Software Engineering, or related technical field with 15-20 years of extensive hands-on experience in distributed systems development, DevOps practice. Advanced degree is a plus
- Contributions to open-source projects related to distributed systems or performance engineering
· Experience in analyzing and documenting system performance metrics across network, compute and storage layers
· Prior experience in mentoring and developing technical talent
· Understanding of AIML infrastructure and any prior experience on RDMA, Roce V2 will be an added advantage
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