• Built a consultancy focusing on providing AI, infrastructure and software solutions.• Re-designed and re-implemented client’s entire product to remove years of tech debt. Re-engineered their cloud pipelines to reduce costs and increase efficiency.• Led core transformation project of software, data and cloud infrastructure, leading (3+) engineers through goals and sprint planning. Decided on key software design strategies.• Developed customisable AWS sandbox account deployment tool for infrastructure deployments and testing for robotics company.• Advised startups on POC development, AI requirements and promising use-cases, GDPR compliance, security considerations and data accumulation strategies.• Provided data cleanup advice along schema design suggestions and database.• Led and managed software engineers both on backend, frontend and infrastructure projects.• Advised on code best practices, CI/CD pipelines, AWS services, security practices and more.• Created and presented custom demos for C-suite audience.• Developed instructional material for junior developer including AWS, Docker, CI/CD (Azure pipelines, CircleCI), Git Flow, and python best practices. Gave masterclasses designed practical work and conducted personalised code reviews. Alumni from the program quickly converted learnings into new full-time positions.• OpenSource contributions to a variety of projects.

• Identified and organised partnership with Imperial College London to work with MSc Students over a period of 5 months to build 2 new potential product offering.• Investigation and implementation of 2D and 3D generation techniques such as DreamFusion.• Developed Machine Learning algorithms for tagging, classifying and recommending 3D assets. RAG pipeline using LLMs and Vector Search to efficiently embed 3D models for search and generation.• Created and led interview process for ML engineer and Full-stack software engineer. Activities include design of take-home coding, CV-screening, conducting interviews.• Planned and executed the delivery of a Proof of Concept of a product which was presented to multiple major players in the video game industry. The POC was very successful and attracted multiple clients. The POC included a React frontend, a python backend deployed on AWS with (among others) DynamoDB, AWS lambdas, AWS APIGateways.• Planned and developed the MVP for the above product using Agile methodology. Product included a State Machine (AWS Step function), an SQL database, several lambdas and batch jobs, APIGateways with authentication.

• Meshing reconstruction and texturing: New product development to create realistic and textured meshes from house interior point clouds. Project was launched to increase competitiveness against other larger players (Matterport, Leica, Microsoft).– Led all project phases from development, through testing and productionisation on both research and implementation.– Implemented mesh reconstruction, UV mapping, mesh texture infilling and denoising for point clouds, meshes and images.– Built a web-based ThreeJs visualiser for the generated meshes to facilitate project development and testing.– Deployed the production pipeline with cloudformation as an AWS step function, coordinating AWS batch jobs and AWS lambdas.• 2D semantic segmentation: The purpose of this project was to obtain a 3D semantic point cloud via image data.– Performed semantic segmentation on images (2D) and projected the predictions onto point clouds (3D) via UV mapping.– Implemented multiple SOTA methods including various of the DeepLab models or Context Encoding• DevOps:– Packaged and released several C++/Python projects as wheels, including the writing of python hooks using pybind11.– Introduced and led the adoption of poetry (python dependency management and packaging tool) by the team, to allow much safer deployments and decrease production issues.– Created a tool to automate project creation to ensure consistency across projects in the team and to decrease learning curve for new members. Tool includes: setting up project documentation, unit tests, Dockerfile, CircleCi, Cloudformation.– Wrote GraphQL queries to interact with our database. Facilitated the transition from JSON files to database queries to get the data through the pipeline.• Research reading group:– Coordinated weekly team reading group aimed to discuss SOTA papers to improve the team’s knowledge of the latest advances.– Presented a range of papers from Graph Neural Networks, NERF to Reinforcement Learning for robotics.

Working on Machine Learning solutions to compliment biomedical investigtions into cancer treatment. Key activities include:- Developing a cell detection algorithm using machine learning and OpenCV to process 3D cancer images.- Creating generic evolutionary optimisation and decision tree libraries for hyperparameter tuning and explainable classification.- Implementing CI pipeline for easy & automatic documentation generation.

Internship at the R&D department focusing on AI solutions.- Worked on developing the Active Wellness project for Faurecia in collaboration with SystemX.- Supported Faurecia’s transition to a new method of data acquisition through organising a campaign to recreate a database.- Reworked elements of the data acquisition process and performed extensive data processing using C language.- Proposed and designed an evaluation protocol for the machine learning models including a Convolutional Neural Network.- Presented a proof of concept to the team; following my team manager's approval, presented to the Software Engineering Director who aproved it for further use.