PerkyLoop
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With over 16 years of experience in the IT industry, I’ve built a career focused on software
quality,
automation excellence, and pragmatic engineering practices. My journey began with hands-on manual
testing and evolved into designing and delivering scalable, enterprise-grade test automation
frameworks
that reduce risk, cost, and time-to-market.
Today, I operate in an AI-augmented engineering environment, leveraging IDE-integrated agents and
advanced language models to accelerate development workflows. This includes orchestrating tools such
as
GitHub Copilot, ChatGPT, Claude (Opus/Sonnet), Gemini, and other emerging agent-based systems to
support
test design, code generation, debugging, and large-scale refactoring.
Rather than treating AI as a standalone tool, I apply it as part of a controlled engineering
workflow-selecting the right model for the task, validating outputs rigorously, and ensuring
quality,
maintainability, and correctness remain uncompromised. AI acts as a force multiplier, while
architectural decisions, engineering judgement, and accountability remain firmly human-led.
An Agile/Scrum practitioner and mentor, I enjoy guiding teams, shaping quality strategies, and
improving
engineering maturity through automation, data-driven insights, and thoughtful adoption of modern
engineering practices.
Designing and evolving scalable, enterprise-grade test automation ecosystems across UI, API, and integration layers. Focused on reliability, maintainability, and fast feedback cycles-enabling teams to ship with confidence while reducing manual overhead at scale.
Delivering pragmatic performance engineering using tools like JMeter to simulate real-world load, identify bottlenecks early, and validate system resilience under production-like conditions.
Driving quality as a system, not a phase-establishing strategies, governance, and engineering practices that embed quality into delivery pipelines. Mentoring teams to improve maturity, ownership, and long-term sustainability.
Operating within an AI-assisted engineering workflow using IDE-integrated agents and modern tooling (Copilot, Claude, Gemini, etc.) to accelerate development, refactoring, and test creation-while maintaining full control over design, correctness, and architecture.
Applying large language models as part of structured workflows for test design, debugging, analysis, and documentation. Selecting the right models for specific tasks and validating outputs rigorously to ensure production-grade quality.
Leveraging AI-assisted techniques for test design, dynamic data generation, API analysis, and faster defect investigation. Integrated into structured workflows to improve speed and insight-while keeping all decisions grounded in engineering judgement.
“ My goal is to help teams evolve from Automation-Enabled Delivery to AI-Assisted, Judgement-led Quality Engineering. ”
AI-augmented development using IDE-integrated agents and multi-model workflows
Developing automation and tooling across multiple languages
Implementing structured and behaviour-driven testing approaches
Designing scalable automation frameworks across UI, API, and integration layers
Robust API validation and contract testing across distributed systems
Testing asynchronous and message-driven architectures
Validating system behaviour under load and stress conditions
Building and maintaining reliable automated pipelines
Working with cloud-native and containerised environments
Working with structured and distributed data systems
Ensuring visibility and insights into system quality and performance
Driving team alignment and efficient delivery practices
Ready to discuss your next project or explore collaboration opportunities? I'd love to hear from you and discuss how we can work together to achieve excellence in quality assurance and test automation.