**Introduction** 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