# The AI/ML Engineer Roadmap Nobody Actually Finishes (But You Should Try)

> Source: <https://dev.to/adityagrawal45/the-aiml-engineer-roadmap-nobody-actually-finishes-but-you-should-try-41el>
> Published: 2026-08-01 15:09:24+00:00

**Okay real talk — if you've googled "how to become an AI/ML engineer" you've drowned in roadmap images that are just... a wall of logos. TensorFlow logo, AWS logo, Docker logo, arrows everywhere, zero actual explanation of what to do with any of it.**

So here's an actual phase-by-phase breakdown, with checkpoints, so you know when to move on instead of doom-scrolling more "10 skills you need" listicles.

Not a whole degree. Just:

**Checkpoint:** Build linear regression from scratch in NumPy. No sklearn. Yes it's annoying. Do it anyway.

Pandas + SQL + feature engineering. This is 80% of the actual job and 5% of what tutorials cover. SQL specifically will show up in interviews and catch people off guard constantly.

**Checkpoint:** Full EDA project on a real (messy) dataset, pushed to GitHub with a real README.

Regression → trees → random forest → gradient boosting (XGBoost, everyone uses this) → SVM → clustering.

Metrics matter more than the algorithm honestly. Accuracy is basically a trap metric for imbalanced data and a shocking number of people don't know that.

**Checkpoint:** 2-3 full projects w/ a writeup of *why* you picked the metrics you did.

Neural net basics → pick PyTorch or TF (I'd say PyTorch) → CNNs for vision → Transformers for basically everything text-related now, including the RAG/LLM stuff that's all anyone's hiring for currently.

**Checkpoint:** Fine-tune a pretrained model, wrap it in a FastAPI endpoint.

Pick ONE:

Learn IAM, storage, Docker, basic Kubernetes, managed training, model registries, endpoints. These concepts transfer no matter which cloud you pick.

**Checkpoint:** Deploy a model you already built. Full loop: train → register → serve.

This is the "actual engineer" phase. Experiment tracking (MLflow), pipelines (Airflow), CI/CD (GitHub Actions), monitoring for drift.

**Checkpoint:** One full pipeline start to finish, automated, monitored.

Stop building 20 tutorial projects that all look the same. Build 3-5 real ones and take them all the way to actually deployed + monitored + documented. That's the difference between a portfolio that gets ignored and one that gets you an interview.

Drop a comment — what phase are you stuck on?