Senior Cloud & AI Engineer | Solutions Architect
Driving Innovation with Agentic AI & Cloud Architecture | AWS & Azure
About Me
As an AWS Certified Solutions Architect and Cloud & AI Engineer with over 5 years of experience in technical consulting, I'm passionate about helping organisations solve complex problems by designing and deploying scalable, secure cloud architectures. My expertise spans cloud infrastructure design, modern application development, and AI integration, with a strong focus on AWS cloud solutions and architectural best practices.
My journey into AI began with a deep academic dive during my honours thesis, where I researched the robustness and of Large Language Models and fine-tuned models. Work that cemented my commitment to the principles of Responsible AI. This foundational knowledge, combined with my architecture expertise, enables me to design comprehensive cloud-native solutions that are both innovative and secure.
Today, I apply that knowledge in a practical setting, leading hands-on technical engagements like Proof-of-Concepts (PoCs) to drive the adoption of modern cloud architectures and AI technologies. I thrive on bridging the gap between technical possibility and business value, acting as a trusted advisor to developers and senior stakeholders alike. I am actively deepening my expertise in the Microsoft AI stack, eager to help customers build the next generation of intelligent, agentic applications.
Core Competencies
A selection of my technical skills and areas of expertise.
Career Journey
A timeline of my professional experience and growth.
Project Spotlights
Highlights of my work demonstrating technical leadership and business impact.
AI Research: LLM Robustness
My honours thesis explored a critical component of Responsible AI.
Thesis: Evaluating the Robustness of Large Language Models
My research investigated the reliability and security of LLMs against adversarial attacks. This exploration is fundamental to deploying trustworthy AI systems, a core principle of Responsible AI.
I developed and applied evaluation frameworks to benchmark model performance, providing insights directly relevant to the secure deployment and fine-tuning of modern AI applications. This work demonstrates a foundational expertise in the principles behind Microsoft's Responsible AI framework.
The chart conceptually illustrates how applying robustness techniques can significantly improve a model's defense against various types of adversarial attacks, making it more reliable for enterprise use.