Machine Learning Engineer
Design, build, and deploy ML models that power products.
AI Career Guide Updated playbook
What is an AI career today?
Your guide to Machine Learning, Data Science, and AI Engineering roles. Learn the skills, tools, and paths teams expect from modern AI talent.
4
Core tracks
4
Step roadmap
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Essential tools
An AI career encompasses roles in Machine Learning, Data Science, AI Engineering, and related fields that focus on building intelligent systems and extracting insights from data.
Popular paths
Choose the role that fits your strengths.
Each path blends coding, data, and product impact. Start with the lane that matches how you like to build.
Data Scientist
Analyze complex data sets to produce business-ready insights.
AI Engineer
Implement intelligent systems across NLP, agents, and vision.
Data Engineer
Build infrastructure for data generation and ML pipelines.
Getting started
Essential skills
- Programming: Python is the most important language.
- Math: Linear algebra, calculus, probability.
- ML: Algorithms, evaluation, deployment.
- Data: SQL, pandas, numpy.
Learning path
4-step roadmap
1
Python programming fundamentals
2
Statistics and math for ML
3
Build projects and a portfolio
4
Network and apply for positions
Tech stack
Tools you will use daily
Industry demand
Where AI talent is hired
High demand across Tech (Google, Meta), Finance, Healthcare, Automotive, and E-commerce.
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