Professional Overview
I'm an AI/ML engineer who builds machine learning systems from raw data through to something people can use. I work in Python with Scikit-learn, TensorFlow and PyTorch, and most of my effort goes into the parts that decide whether a model works in practice: understanding the data, choosing the right approach, and checking that results hold up outside the notebook. I'm comfortable across the full workflow, including data cleaning and exploratory analysis, feature engineering, model training and evaluation, and turning results into clear insights for people who aren't technical. I prefer simple models that are well understood over complex ones that can't be explained. I'm looking for a team working on real problems with real data, where I can build, learn fast and take ownership of what I ship.
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.
Automation & AI Workflows
Build intelligent automation pipelines, integrate AI APIs into existing tools, and replace manual workflows with agentic AI systems.
MLOps & AI Infrastructure
Architect scalable ML infrastructure — CI/CD for ML, model versioning, experiment tracking, monitoring, and cloud deployment on AWS, GCP, or Azure.
NLP & Prompt Engineering
Build natural language processing systems, fine-tune large language models, and craft reliable production prompts for AI-powered products.
