AI/MLOps Engineer

1. 1
   Build Python and DevOps Foundations

   MLOps requires proficiency in: Python (for pipeline code and automation scripts), Docker (containerizing ML workloads), Kubernetes (orchestrating containerized ML services at scale), CI/CD tools (GitHub Actions, Jenkins, or GitLab CI for automating training and deployment pipelines), and Linux/bash scripting. The DevOps foundation is as important as the ML knowledge — MLOps is DevOps applied to ML systems. Kubernetes Certified Administrator (CKA) or Kubernetes Application Developer (CKAD) provides the container orchestration credential.

   Python + Docker + Kubernetes + CI/CD
2. 2
   Learn ML Fundamentals and Model Lifecycle

   MLOps engineers need to understand what they're operationalizing: ML model training, evaluation metrics, hyperparameter tuning, and model versioning. Deep mathematical knowledge of ML is not required, but understanding what a model is, how training works, and what can go wrong in production (data drift, concept drift, training-serving skew) is essential context for building reliable ML infrastructure.

   ML fundamentals for MLOps context
3. 3
   Master MLOps Tooling — MLflow, Kubeflow, SageMaker

   Core MLOps platforms: MLflow (open-source experiment tracking, model registry, and serving — the most widely used tool), Kubeflow (Kubernetes-native ML pipeline orchestration), AWS SageMaker (end-to-end managed ML platform — the dominant enterprise cloud ML platform), and Google Vertex AI (GCP's equivalent). Feature stores: Feast (open source), AWS Feature Store, Tecton. Model monitoring: WhyLabs, Arize, Evidently AI. Building hands-on projects with these tools is the only way to develop real competency.

   MLflow + Kubeflow + SageMaker/Vertex AI
4. 4
   Build an End-to-End ML Pipeline Portfolio Project

   Portfolio project for MLOps roles: build a complete automated ML pipeline that trains a model on a schedule, logs experiments to MLflow, stores the best model in a model registry, serves it via a FastAPI endpoint containerized in Docker, and monitors prediction drift with automated alerts. Host it on GitHub with clear documentation. This demonstrates the full MLOps lifecycle and is significantly more impressive than notebook-level ML projects.

   Full MLOps pipeline — portfolio project
5. 5
   Earn Google Professional ML Engineer or AWS ML Specialty

   Both credentials validate production ML platform competency. Google Professional ML Engineer ($200) emphasizes Vertex AI, BigQuery ML, and Google Cloud ML architecture. AWS Machine Learning Specialty ($300) emphasizes SageMaker, the AWS ML service ecosystem, and production ML deployment patterns. Either opens roles at cloud-forward companies. The NVIDIA Deep Learning Institute (DLI) also offers MLOps and production AI certifications for more specialized roles.

   Google Prof ML Engineer or AWS ML Specialty

• 9:00 AMModel drift alert — WhyLabs monitoring dashboard shows the customer churn model's input feature distribution for "days since last login" has drifted significantly over the past 5 days (PSI = 0.28, threshold 0.20). Investigate: a product change caused the login tracking to be updated. The model is receiving different feature values than it was trained on. Trigger an emergency retraining pipeline.
• 10:00 AMRetraining pipeline — the Kubeflow pipeline for churn model retraining kicks off: data extraction from BigQuery (last 90 days), feature engineering, model training (XGBoost), hyperparameter tuning (Optuna), evaluation against holdout set. All steps containerized in Docker, orchestrated in Kubeflow, experiment logged in MLflow.
• 11:30 AMModel registry — the retrained model achieves AUC-ROC 0.88 vs. the current production model's 0.84 (degraded from drift). Promote the new model from Staging to Production in MLflow. Tag the deployment version in Git. Update the SageMaker endpoint using blue-green deployment.
• 12:00 PMLunch — 30 minutes.
• 1:00 PMFeature store work — onboarding a new ML team feature: user payment behavior features for the fraud detection model. Set up Feast feature definitions, configure the offline store (BigQuery) and online store (Redis), write the materialization job. Coordinate with the data engineering team on upstream pipeline dependencies.
• 3:00 PMInfrastructure as code — update the Terraform configuration to provision a new GPU instance type for the image classification training cluster. Test in staging environment, review the cost implications ($2,100/month vs. current $1,400/month), document the tradeoff for the architecture review.
• 4:30 PMOn-call review — audit this week's model performance alerts. 3 alerts: one resolved (the churn model today), one acknowledged (minor data quality issue in the recommendation feature pipeline — fix scheduled for tomorrow), one new (model latency spike in the fraud detection API — investigate). Triage and assign.

MLOps engineers build and maintain the infrastructure for deploying and monitoring AI/ML systems in production — the operational backbone of AI at scale. This role is created by AI adoption, not threatened by it. The +45% growth is one of the highest in the catalog and reflects the genuine infrastructure gap in AI deployment.