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Job Description - Software Engineer Performance Tooling Specialist

At ABB's Process Automation Digital Quality Engineering Center of Excellence (PADI QE COE), you'll be at the heart of delivering trust, reliability, and innovation for our customers. This is not a traditional QA role - it is an opportunity to become part of a specialist engineering team that drives quality across every layer of the Genix IIoT and APM platforms, from DataOps pipelines and Edge connectivity to AI/ML validation, visualization, and customer solutions.

You will contribute directly to building automation-first frameworks, integrating test packs into CI/CD pipelines, and enabling functional and non-functional coverage that ensures our products meet the highest standards of performance, security, and scalability. Working alongside domain experts in energy, marine, manufacturing, and operational excellence, your role will help transform industrial operations globally.

At PADI QE COE, your work is not just about testing - it's about enabling industries to operate smarter, safer, and more sustainably.

Position Details

Position Title: Software Engineer Performance Tooling Specialist
Grade: G12
Location: ABB Innovation Center (AIC), Brigade Southfield, Hoodi, Bangalore
Reports To: Head of Quality Engineering COE

Role Overview

Responsible for building and managing performance toolchains and baselines for Genix and APM.

Key Responsibilities

. Design and implement load, stress, and soak tests.
. Integrate performance suites into pipelines.
. Establish performance baselines and SLAs with engineering teams.
. Troubleshoot performance bottlenecks.

Skills & Competencies

. Performance tools (JMeter, LoadRunner).
. Monitoring (Grafana, Prometheus, ELK).
. Kubernetes and cloud performance.
. Python and scripting knowledge.

Experience & Education

. 10-14 years of experience (G12).
. B.Tech/M.Tech from reputed institutions.
. Ability to lead performance test design and execution.

Expected Outcomes / KPIs

. Performance baselines established for all releases.
. Performance regressions identified pre-production.
. Improved system scalability and resilience.

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