ML Engineer Roadmap¶

In one line: A staged plan from software or data skills to a classical-plus-LLM ML engineer role, with what to learn, what to build and how it differs from the AI engineer path. Last reviewed: 2026-11-07

The short version¶

• An ML engineer trains, evaluates, deploys and monitors models, including ones you train yourself. An AI engineer mostly builds on models someone else trained. The line is blurry; see the AI careers map.
• Order of learning: Python and data handling, classical ML and evaluation, deep learning, then serving and monitoring, then LLM-specific training and fine-tuning.
• Hiring teams look for end-to-end evidence: a model you trained, measured honestly, deployed and watched. Notebooks alone are weak evidence.
• This page is judgment drawn from this site's own guides. It contains no salary or job-market figures because none were sourced.

Stage 1: foundations¶

• Python for AI, notebooks and Colab, maths for ML without fear.
• SQL and data handling. The data site covers this in depth.

Stage 2: classical ML and honest evaluation¶

• What is machine learning, supervised vs unsupervised, training, validation and overfitting.
• Decision trees and gradient boosting, feature engineering, time series, recommenders.
• Build: tabular ML end to end. Keep a simple baseline and beat it by a measured margin.

Stage 3: deep learning¶

• Neural networks in plain English, CNNs, transformer architecture, reinforcement learning basics.
• Read a few foundational papers with the summaries: Attention Is All You Need.

Stage 4: production¶

• Deploy an ML model, set up an eval harness, GPUs for AI, inference servers, quantization and distillation.
• Learn what breaks after launch: data drift, silent label changes, training-serving skew, cost. The cloud site helps with the infrastructure side.

Stage 5: LLM-era skills¶

• Fine-tuning vs RAG, then fine-tune with LoRA and run Llama on a single GPU.
• How LLMs are trained and evals for LLMs.

What to show¶

Pick three artefacts: a tabular model with a leakage-safe evaluation, a deployed model with monitoring, and one fine-tune with before and after numbers. Package them as in build an AI portfolio.

Common mistakes¶

• Jumping to deep learning before you can beat a baseline with gradient boosting.
• Reporting one lucky split. Use a held-out set you touched once.
• Treating deployment as someone else's job.

Next steps¶

• ML engineer path (the learning order) and ML engineer interview.
• Data scientist in the LLM era if you lean toward analysis.
• AI certifications guide.

Sources¶

• Judgment: the staging above is this site's editorial view, not drawn from a hiring survey.
• Linked pages on this site hold the sourced detail for each topic.