As part of Mory’s technical team, I support early-stage start-ups by developing software solutions and contributing to system design and infrastructure across the accelerator’s portfolio.

Key Contributions:

• Worked closely with development teams to design and implement backend services, APIs, and data structures tailored to fast-moving start-up environments.

• Assisted in shaping technical architecture for MVPs, ensuring systems are scalable, maintainable, and ready to grow.

• Helped establish CI/CD pipelines, Single Sign-on platform, containerised deployments, and cloud-based infrastructure to streamline development and delivery.

• Collaborated with the VP Engineering to maintain consistency and quality across a variety of ventures.

• Contributed to shared documentation, internal tooling, and reusable components used throughout the accelerator’s ecosystem.

As part of the MiSAR research project under Dr Nour Ali, I contribute to the evaluation and enhancement of a model-driven tool designed to recover architectural models from microservice-based systems.

Key Contributions:

• Evaluating and testing the MiSAR toolchain across different environments and systems to assess reliability, usability, and reproducibility.

• Analysing microservice-based applications to understand how implementation artefacts (e.g. Docker, configuration, environment, code) are transformed into architectural models.

• Contributing to tool improvement and development, including implementing fixes, refining workflows, and improving overall user experience.

• Exploring and extending the tool’s capabilities to support additional ecosystems such as PHP and Go, beyond its current Java-focused scope.

• Identifying architectural and tooling limitations and proposing enhancements to improve integration, scalability, and accessibility.

• Collaborating on research-driven development, bridging practical software engineering with model-driven architecture recovery approaches.

Building on my internship experience, I continued at Veunex as an Applied Machine Learning Engineer and Technical Representative, supporting the development of AI-based HSE (Health, Safety, and Environment) solutions for oil and gas industry clients.

Key Contributions:

• Assisted in developing and tuning object detection and image classification models for real-world HSE applications, improving performance and reliability in production environments.

• Helped refactor the data pre-processing workflow by connecting previously manual steps into a unified pipeline and enabling parallel execution of tasks, reducing total processing time from around ~two weeks to under 24 hours.

• Supported the implementation of data security and privacy measures within the ML lifecycle to align with client and industry requirements.

• Collaborated with software and ML teams to integrate AI modules into end-user applications, helping ensure technical work matched business and operational needs.

• Participated in client meetings and product demos alongside senior staff, representing Veunex’s AI capabilities to clients such as Petronas, PDO, OQ, and Saudi Aramco Nabors Drilling.