Junior AI Engineer

Build and deploy AI/ML systems in production

• Field: AI & Machine Learning

What the job is

Junior AI Engineers build, train, and deploy machine learning models in production environments. They work at the intersection of data science and software engineering, focusing on making AI systems reliable and scalable.

Routes there

AI & Machine Learning

Become a junior AI engineer (math → ML → deep learning → deployment).

28 weeks · Intermediate

1. Python & Mathematics for ML4 chapters · 10 hours a week · 1 checkpoint
2. Machine Learning Fundamentals4 chapters · 12 hours a week · 1 checkpoint
3. ML Engineering & Deployment3 chapters · 8 hours a week · 1 checkpoint

Start this track

The skills these routes build

From the chapters of each learning path, in the order they are taught.

• Python & Mathematics for ML: NumPy · pandas · Jupyter · SciPy · Vectors · Matrices · Eigenvalues · SVD · PCA · Derivatives · Gradients · Gradient Descent · Backpropagation · Probability · Bayesian Reasoning · Probability Distributions · Maximum Likelihood Estimation
• Machine Learning Fundamentals: Linear Regression · Regularization · Feature Engineering · Cross-Validation · Logistic Regression · Decision Trees · Random Forests · XGBoost · Hyperparameter Tuning · k-means Clustering · Hierarchical Clustering · DBSCAN · PCA · Dimensionality Reduction · Neural Networks · Backpropagation · PyTorch · TensorFlow · Keras
• ML Engineering & Deployment: Experiment Tracking · MLflow · Weights & Biases · DVC · Project Structure · FastAPI · Model Serving · Docker · Batch vs Online Inference · Model Monitoring · Data Drift Detection · Retraining Pipelines

What is not promised

Artimend prepares you for this work; it does not find you a job. Nor does it publish salary or demand figures, because it has none it can source.