If you are building a machine learning portfolio today, it is time to archive your “Titanic Survival Prediction” and “Iris Flower Classification” repositories. The baseline for what makes a competent Machine Learning Engineer has shifted dramatically over the last couple of years. Hiring managers in 2026 are no longer impressed by a static Jupyter Notebook showing a 95% accuracy rate on clean, pre-packaged CSV files. Today, companies are looking for engineers who can deploy models, manage data drift, ground Large Language Models (LLMs) in enterprise data, and build autonomous agents. If you want your GitHub profile to catch the eye of a tech recruiter or lead architect this year, here are four machine learning projects you should actually build. The Real-World MLOps Pipeline: Continuous Fraud Detection The Goal: Show you can put a model into production and keep it healthy. The Tech Stack: Python, FastAPI, Docker, GitHub Actions, MLflow.…