Quality Engineer

Fintech, Automotive & Trading Platforms

I catch bugs before they become problems.

Experienced

Test Automation Engineer

QA/QE/SDET | 10+ years | Front & Back End | Java | Ford, Aviva | Now at Create Future (FanDuel, Wood Mackenzie, Evoke) | London

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A Little About Me

I Love To Travel

Studying in the U.S. broadened my worldview and sparked a love for diversity and exploration. I’ve since lived in Michigan, Warsaw, and London—and traveled to 60+ countries.

Full-Stack Testing

My experience includes frontend testing of user interfaces and backend testing of services, data handling, and application logic.

Front End

Back End

Git Repositories

Mainly basic projects and practice work I can revisit or use as quick references. It’s a simple archive of things I’ve built and learned from.

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Digital Shelf

It’s more of a personal collection—random things I come across, resources worth remembering, and thoughts I might want to come back to later. Just a place to keep track of it all.

GitHub Copilot - Notes: Spaces

Exercise: https://github.com/skills/scale-institutional-knowledge-using-copilot-spaces What is Copilot Spaces: A dedicated Copilot chat with curated context Uses selected: Files Issues Pull requests Custom instructions More focused than general Copilot Chat Key Difference vs Copilot Chat: Copilot Chat → broad, general suggestions Spaces → focused, context-driven responses Why Spaces Improve Quality: Narrow context → more accurate and consistent answers Better for repeatable a

Guthub Copilot - Notes: Prompt Engineering

What is Prompt Engineering: Process of writing clear instructions for AI (Copilot) Ensures generated code is correct, relevant, and useful 4 Core Principles (4 Ss): Single → focus on one task Specific → give clear, detailed instructions Short → keep prompts concise Surround → provide context (files, comments, open tabs) Best Practices: Be clear and explicit Provide context using comments or related files Use examples to guide output Iterate: Refine prompt instead of rewriting

GitHub Copilot - Notes: Part 1

Exercise: https://github.com/skills/getting-started-with-github-copilot Mitigating AI Risks AI provides benefits (innovation, efficiency) but also introduces risks Main Risks: Lack of transparency → AI decisions are difficult to understand Bias and harmful outcomes → unfair decisions, privacy issues Risk Mitigation: Governance frameworks → establish rules and policies for AI use Transparency → ensure AI systems are explainable Human oversight → humans monitor and guide AI dec

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