Professional Machine Learning Engineer

Design scalable ML training pipelines on Vertex AI using Vertex AI Training and custom containers.

Select the appropriate ML framework and hardware accelerators (TPUs, GPUs) for a given use case.

Design feature engineering workflows using Vertex AI Feature Store and BigQuery ML.

Apply data preprocessing and augmentation strategies for different data types (tabular, image, text, time series).

Design ML system architectures considering latency, throughput, cost, and interpretability tradeoffs.

Build data pipelines using Dataflow, Cloud Composer (Apache Airflow), and Vertex AI Pipelines.

Perform exploratory data analysis and identify data quality issues using BigQuery and Vertex AI Workbench.

Apply feature engineering techniques (normalization, encoding, embeddings, feature crosses).

Handle imbalanced datasets using resampling, class weighting, and synthetic data generation.

Ensure data governance, privacy (differential privacy), and lineage tracking.

Build and train models using TensorFlow, PyTorch, and AutoML on Vertex AI.

Implement hyperparameter tuning using Vertex AI Vizier and Keras Tuner.

Apply transfer learning and fine-tuning techniques for foundation models and LLMs.

Evaluate model performance using appropriate metrics (AUC, F1, RMSE, perplexity).

Debug and improve ML model training using profiling, learning curves, and error analysis.

Design and implement CI/CD/CT pipelines for ML using Vertex AI Pipelines and Cloud Build.

Automate model retraining triggers based on data drift, concept drift, or performance degradation.

Implement MLOps best practices including experiment tracking, model registry, and artifact versioning.

Orchestrate multi-step pipelines using Kubeflow Pipelines and TFX components.

Deploy models to Vertex AI Prediction endpoints (online, batch, edge).

Monitor model performance and detect data/concept drift using Vertex AI Model Monitoring.

Optimize model serving latency and throughput using quantization, distillation, and caching.

Implement responsible AI practices (fairness, explainability with Vertex Explainable AI, bias detection).

Manage model versions and roll back deployments safely.