Machine Learning Engineer specialising in building, training, and deploying production-grade M
L systems.
Experienced across the f
ull M
L lifecycle, including data ingestion and cleaning, feature engineering, model
trai…
Machine Learning Engineer specialising in building, training, and deploying production-grade M
L systems.
Experienced across the f
ull M
L lifecycle, including data ingestion and cleaning, feature engineering, model
training, evaluation, and cloud deployment. Strong background in computer vision, applied deep learning
(InceptionV3, ResNet), and M
LOps, with proven delivery of cost-efficient M
L pipelines in healthcare and
enterprise environments. Experience working with vision-based representation learning and strong
foundational knowledge applicable to multimodal and vision-language model architectures. Passionate
about applied AI in interactive systems and gaming environments, with prior experience contributing to
large-scale software systems in the games industry.
Collaborated with back-end developers to generate roadmaps and develop use case stories, driving the creation of new features and resolution of existing issues.
Active involvement in future planning, sparking innovative ideas and uncovering new business opportunities, white-labeling, to drive growth and competitive advantage.
Collaborated with back-end developers to generate roadmaps and develop use case stories, driving the creation of new features and resolution of existing issues. Active involvement in future planning, sparking innovative ideas and uncovering new business opportunities, white-labeling, to drive growth and competitive advantage.
Developed computer vision models namely Inceptionv3, Resnet, and Imagenet for image classification of DDISM database images using Python, Keras, and Scikit-Learn in collaboration with the University of Surrey for student training.
Saved processing time and development costs by rewriting Bash scripts to dynamically pull data from Google Cloud, integrated with a MongoDB database connected to a server that processes website requests.
Sourced DDISM files from GCP using Bash scripts for sorting, cleaning, and feature extraction using Excel, NumPy, and Pandas for use in future model training and evaluation.
Developed Computer Vision Models namely Inceptionv3, Resnet, Imagenet for Image Classification of DDISM Database Images using Python, Keras, Scikit-Learn in collaboration with the University of Surrey for student training. Saved processing time and development costs by rewriting Bash scripts to dynamically pull data from Google Cloud, integrated with a MongoDB database connected to a server that processes website requests. Sourced DDISM files from GCP using Bash scripts for sorting, cleaning and feature extraction using Excel, cleaning using numpy and pandas for use in future model training and evaluation.
Developed, coded, implemented and documented new features added to the product application to meet consumer demand.
Documented, tracked, and communicated all code related activities on Jira, GitHub, and to shareholders, stakeholders, and team members to equip the sales team with relevant information for exposure.
Developed, coded, implemented and documented new features added to the product application to meet consumer demand. Documented, Tracked, Communicated all code related activities on Jira, GitHub and to shareholders, stakeholders and team members to equip sales team with relevant information for exposure.
UK Greenhouse Gas Emission Prediction using ML Regression ModelTrained Multi-Linear Regression Model (scikit-learn) to predict Greenhouse Gas Emissionsin the United Kingdom over the next 5 years, R2 = 0.98.
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