📍 Updated May 2026

ML Engineer Roadmap

Phase 1

Foundation

Build the conceptual base. Every ML Engineer interview tests these.

  1. Machine Learning Specialization

    Beginner

    DeepLearning.AI + Stanford Online

    Covers supervised, unsupervised learning, and neural network basics. This is the universal starting point every ML Engineer knows.

    ~3 months self-paced · 2 weeks if focused

    Start Course

  2. Deep Learning Specialization

    Intermediate

    DeepLearning.AI

    CNNs, RNNs, Transformers, NLP — the backbone of every ML Engineer role. Directly referenced in technical interviews.

    ~3 months self-paced · 2–3 weeks focused

    Start Course

Phase 2

LLM & GenAI Core

This is what gets you hired in 2025–2026. Every ML Engineer JD mentions at least 3 of these topics.

  1. Generative AI with Large Language Models

    Intermediate

    AWS + DeepLearning.AI

    LLM lifecycle, transformers deep dive, fine-tuning, and deployment. Co-built with AWS so you get cloud exposure automatically.

    3 weeks

    Start Course

  2. Retrieval Augmented Generation (RAG)

    Intermediate

    DeepLearning.AI

    RAG is the most in-demand LLM skill right now. After this course you can build a deployable project immediately. Every AI startup uses RAG.

    1 week

    Start Course

  3. LangChain for LLM Application Development

    Beginner

    LangChain + DeepLearning.AI

    Industry-standard tool for building LLM apps. Pair this with the RAG course and you have a full project to show recruiters.

    1 week

    Start Course

  4. Agentic AI

    Intermediate

    DeepLearning.AI · taught by Andrew Ng

    AI Agents are the fastest-growing topic in ML hiring right now. Andrew Ng teaches this directly. Do not skip.

    1–2 weeks

    Start Course

Phase 3

MLOps & Production

What separates ML Engineers from data science students. Hiring managers look for this.

  1. Machine Learning in Production

    Intermediate

    DeepLearning.AI

    MLflow, model monitoring, deployment pipelines, data drift — the full MLOps picture. Directly fills the gap between building models and deploying them.

    2 weeks

    Start Course

  2. Orchestrating Workflows for GenAI Applications

    Intermediate

    Astronomer (Apache Airflow) + DeepLearning.AI

    Airflow appears in most ML Engineer job descriptions. Doing this as a new grad puts you ahead of most applicants.

    1 week

    Start Course

Phase 4

Specialize

Pick one based on the role you want most. Go deep on one track.

  1. Fine-Tuning & RL for LLMs: Intro to Post-Training

    Intermediate

    AMD + DeepLearning.AI

    LoRA, RLHF, DPO — how to adapt foundation models. Required knowledge for Applied AI roles at startups and labs.

    1 week

    Start Course