AI / ML Engineer Roadmap

Go from Python basics to building and deploying real machine-learning and AI agent systems.

A practical AI engineering path focused on shipping, not just theory. You move from programming and data through classic machine learning, deep learning and modern LLM applications, finishing with how to deploy and operate AI in production the way NUEXUS does for clients.

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Duration: 8 to 12 months
Level: Beginner to job-ready
Stages: 6
Field: Artificial Intelligence

By the end

What you will be able to do

Build, train and evaluate machine-learning models

Work with deep learning and modern LLMs

Build retrieval-augmented and agentic AI applications

Deploy and monitor AI systems responsibly in production

The roadmap

Your step-by-step path

Follow the stages in order. Each one builds on the last, from fundamentals to job-ready.

01
Python & Data Foundations
The toolkit every AI engineer builds on.
- Python for data
- NumPy and pandas
- Math: linear algebra & stats
- Data cleaning and exploration
02
Machine Learning Core
Understand how models actually learn and how to judge them.
- Supervised & unsupervised learning
- Model evaluation and validation
- Feature engineering
- scikit-learn workflows
03
Deep Learning
Step up to neural networks and modern architectures.
- Neural network fundamentals
- PyTorch or TensorFlow
- CNNs and transformers
- Training and tuning
04
LLMs & Generative AI
Build with the models reshaping the industry.
- Prompt engineering
- Retrieval-augmented generation (RAG)
- Embeddings and vector search
- Agentic AI patterns
05
MLOps & Deployment
Get models out of the notebook and into production.
- APIs and model serving
- Containers and pipelines
- Monitoring and drift
- Cost and performance
06
Job-Ready: AI Engineer
Ship an end-to-end project and learn to do it responsibly.
- End-to-end capstone project
- Responsible & secure AI
- Portfolio and case studies
- Working with stakeholders
Certification checkpoint NUEXUS AI Engineer (Verified)