Junior MLOps Engineer

Manage ML model lifecycle, deployment, and monitoring

• Field: AI & Machine Learning

What the job is

Junior MLOps Engineers manage the operational side of machine learning, including model versioning, deployment pipelines, monitoring, and retraining. They combine DevOps principles with ML-specific requirements.

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.