Professional Overview
Machine learning engineer focused on MLOps: I take models from a notebook to a deployed, monitored service. Final-year Computer Science student, available to start within a week. PredictAI: an end-to-end predictive maintenance system on the AI4I 2020 dataset, with experiment tracking in MLflow, data versioning with DVC, drift monitoring with Evidently, and a FastAPI model service in Docker. I found that my first evaluation inflated precision by scoring on a balanced holdout, fixed the protocol, and documented the fix in the README. Football possession analysis (Computer Vision internship, IT Gate Academy, 2026): three YOLO models for players (tracked with ByteTrack), team kits and the ball, each trained on its own Roboflow-annotated dataset, combined to compute per-team possession from match video. Before that: remote part-time AI engineer at Lumenex Health (2024), classifying heart sounds from acoustic signals. Stack: Python, PyTorch, scikit-learn, XGBoost, MLflow, DVC, Prefect, FastAPI, Docker, Kubernetes, AWS (EC2, S3, ECR, ECS Fargate, EKS), LangChain, FAISS. Preparing for the CKA exam.
Areas of Expertise
AI & Machine Learning Engineers
Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.
MLOps & AI Infrastructure
Architect scalable ML infrastructure — CI/CD for ML, model versioning, experiment tracking, monitoring, and cloud deployment on AWS, GCP, or Azure.
AI Product & Strategy
Translate AI capabilities into product roadmaps, define success metrics, and drive adoption across engineering and business teams.
