Machine Learning Engineer · Data Scientist · MLOps
Building and shipping ML systems end to end — from data pipelines and model training through to CI/CD, containerized APIs, and production monitoring. Not just notebooks.
Machine Learning Engineer focused on shipping production-ready systems.
I'm a Machine Learning Engineer with 1+ year of hands-on experience building and deploying production ML systems — not just notebooks. I've shipped two production-ready systems: a fraud detection model (79% F1-score, 81% precision) and a customer churn prediction API (84% accuracy), both containerized with real CI/CD pipelines.
I build end-to-end ML pipelines — from data versioning and experiment tracking (DVC, MLflow) through CI/CD deployment (GitHub Actions, Docker, FastAPI) — turning model output into decisions business teams actually use.
My core toolkit is Python, Scikit-Learn, XGBoost, MLflow, DVC, Prefect, Docker, and FastAPI. I'm also actively expanding into deep learning, cloud infrastructure (AWS, Kubernetes), and LLM-based agents.
Looking for Machine Learning Engineer, Data Scientist, or MLOps Engineer roles — entry-level, remote-friendly. Comfortable with async workflows.
Core skills applied in projects, plus additional knowledge I'm actively developing.
My end-to-end workflow for taking an ML idea from data to production.
An honest self-assessment — including what's still in progress.
One formal internship, plus freelance work since.
Each project reflects my end-to-end MLOps approach and business-driven problem solving.
Business Impact: Reduced false positives by 15% through threshold tuning.
Tech Deep Dive: XGBoost with SMOTE for class imbalance. Automated preprocessing with DVC and Prefect. Deployed with FastAPI + Docker on Render.
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Business Impact: Flags at-risk customers proactively, enabling retention teams to act early.
Tech Deep Dive: LightGBM with Optuna tuning. Web interface built with FastAPI + Jinja2.
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Business Impact: Translated findings into targeted marketing recommendations.
Tech Deep Dive: Random Forest with GridSearchCV. SMOTE for class balance. Engineered booking window and route popularity features.
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Business Impact: £7–10M expansion opportunity with 2-month payback, validated within 3% of actual BA figures.
Tech Deep Dive: Built multi-sheet Excel workbook and 6-panel dashboard spanning haul type, time-of-day demand, and tier distribution.
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What mentors and peers have said about working with me.
“Emmanuel’s ability to take a raw idea, structure the data, and ship a containerized API in under two weeks was remarkable. He thinks like an engineer, not just a data scientist.”
“I was impressed by his MLOps discipline. He didn't just train a model; he versioned the data, tracked experiments, and made the whole pipeline reproducible. That's rare for a junior.”
“Emmanuel is collaborative and explains complex ML concepts clearly. He was a key contributor to our fraud detection project, and his deployment work was flawless.”
I write about ML engineering, MLOps, and the lessons I learn along the way.
Open to Machine Learning Engineer, Data Scientist, and MLOps roles.
I'm actively looking for Machine Learning Engineer, Data Scientist, or MLOps Engineer roles, remote-friendly. Whether you have a role, a project, or just want to connect — reach out below.
Machine Learning Engineer with 1+ year of hands-on experience building and deploying production ML systems — including fraud detection (79% F1) and churn prediction (84% accuracy). Builds reliable ML pipelines from data versioning through CI/CD. Open to remote roles.