Job Description
Introduction
The IBM Storage Engineering team is looking for a Performance and Automation Architect to join our Ceph Quality Engineering and Performance organization in Bangalore, India.
In this role, you will lead initiatives to design, build, and optimize performance and CI automation frameworks for large-scale distributed storage systems.
You will collaborate with global teams across development, QE, and infrastructure to drive continuous performance improvements, build intelligent CI pipelines, and integrate AI-based analytics for quality engineering.
This position offers the opportunity to influence architecture decisions, define benchmarking standards, and shape IBM’s enterprise-grade distributed storage validation ecosystem.
Your Role And Responsibilities
Lead end-to-end performance engineering for distributed storage systems and CI frameworks.
Design and develop scalable automation tools for CI/CD, benchmarking, and performance analytics.
Build and maintain workload generators, monitoring dashboards, and test frameworks for large-scale environments.
Triage, analyze, and resolve performance issues across compute, storage, and network layers.
Implement AI/ML-driven insights into CI processes for predictive validation and anomaly detection.
Collaborate with upstream and internal teams to define KPIs, metrics, and performance objectives.
Mentor engineers on performance optimization, automation, and observability best practices.
Required Technical And Professional Expertise
12+ years of experience in Performance Engineering, Quality Engineering, or Automation Architecture.
Strong programming and scripting skills in Python, Bash, or Go.
Hands-on experience with distributed storage systems (Ceph, GlusterFS, MinIO, or similar).
Deep understanding of Linux internals, networking, and storage I/O performance.
Proficiency in CI/CD frameworks (Jenkins, GitLab CI, or similar).
Experience with cloud infrastructure platforms (OpenStack, OpenShift, or Kubernetes).
Familiarity with performance and workload tools (FIO, COSBench, vdbench, or equivalent).
Experience with infrastructure automation (Ansible, Terraform).
Strong problem-solving and analytical skills across distributed environments.
Preferred Technical And Professional Experience
Experience integrating AI/ML-driven frameworks for CI analytics or performance insights.
Knowledge of monitoring and observability tools (Grafana, Prometheus, ELK stack).
Experience in container performance tuning and Kubernetes-based workloads.
Understanding of cloud networking and distributed storage architectures.
Contributions to open-source or distributed systems communities
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