Machine Learning Engineer
Technical role · involves code
Trains, evaluates and deploys machine-learning models, then keeps them working as data shifts underneath them. Sits between data science and software engineering, and is accountable for the model in production.
Also advertised as: ML Engineer, MLOps Engineer.
What the job involves
- Build training pipelines and select features from raw data
- Diagnose overfitting and choose evaluation metrics that survive class imbalance
- Tune optimisation and regularisation rather than accepting library defaults
- Deploy models behind APIs and monitor for drift and degradation
- Retrain and version models as the underlying data changes
Skills this role needs
These are the actual modules taught on the track that train for this role — not a generic skills list.
- AI Foundations — optional basics (free)
- Module 0 — Foundations: Math & Tooling for ML
- ML Foundations
- Classical ML Algorithms
- Data Pipelines
- Model Evaluation
- Neural Networks
- Deep Learning
- MLOps
- Production ML
- Time Series & Forecasting
- Recommender Systems
How to train for it
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