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IBM
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Performance & DevOps Lead

Performance & DevOps Lead

IBM

12 - 17 years

12 - 17 Lacs

bengaluru

Posted:1 hour ago| Platform:

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Skills Required

devops cloud networking kubernetes python ai ansible analytics elk stack automation automation tools grafana linux jenkins storage systems bash gitlab terraform prometheus performance optimization programming

Work Mode

Work from Office

Job Type

Full Time

Job Description

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 education

Bachelor's Degree

Required technical and professional expertise

12+ years of experience in Performance Engineering, Quality Engineering, or Automation Leadership.

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 and container platforms (OpenStack, OpenShift, or Kubernetes).

Proficiency with performance and workload tools (FIO, COSBench, vdbench, or equivalent).

Experience in infrastructure automation (Ansible, Terraform).

Strong analytical, problem-solving, and cross-functional collaboration skills.

Preferred technical and professional experience

Experience implementing 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.

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