AI careers: How to start, what to learn, and which roles pay off

Breaking into AI feels overwhelming, but you can make steady progress with a clear plan. First pick a role you want—machine learning engineer, data scientist, MLOps engineer, research engineer, or AI product manager. Each role needs different depth: engineers build models and production systems, researchers push model ideas, product managers translate requirements into metrics and features.

Start with a tight skill list. Learn Python, NumPy, pandas, and one deep learning framework like PyTorch or TensorFlow. Practice core ML concepts: supervised learning, loss functions, regularization, and basic probability. Build small projects that show end-to-end work: collect data, train a model, evaluate it, and deploy a demo. A simple image classifier, a text sentiment model, or a recommendation proof-of-concept are great starters.

Build a portfolio that gets noticed

Publish code on GitHub with clear READMEs and short demo videos or web apps. Host live demos on Heroku, Streamlit, or simple Flask apps so recruiters can click and test. Add a short case study for each project: problem, approach, results, lessons learned. Include metrics—accuracy, latency, or business impact—not vague claims. A portfolio with two polished projects beats ten unfinished experiments.

Practical learning and interview prep

Use focused courses: Andrew Ng’s Coursera ML, fast.ai practical deep learning, and hands-on Kaggle competitions. Solve real problems on Kaggle to learn data cleaning, feature engineering, and model tuning. For interviews, prepare three areas: coding (Python), ML theory (linear models, neural networks, evaluation), and system design for ML (data pipelines, model serving, monitoring). Practice whiteboard explanations for model choices and trade-offs.

Network with purpose. Join local meetups, follow engineers on Twitter, and contribute to open-source repos. Reach out with a short message that references a project of theirs—don’t send generic templates. Apply for internships or junior roles and take contract gigs to gain production experience. Recruiters value shipped features and teamwork more than theoretical papers at entry level.

Don’t ignore MLOps and product skills. Knowing CI/CD for models, containerization with Docker, and cloud basics (AWS, GCP, or Azure) makes you more hireable. Learn to monitor drift and set up simple retraining pipelines. Product-minded engineers who show ROI in interviews stand out.

Track progress with small milestones: finish a course, complete a project, deploy a demo, and apply to N jobs weekly. Expect rejections—treat each one as data. Update your resume and portfolio after every interview with feedback. Over months you’ll build momentum and land that role.

Expect salaries to vary by location and role. Junior ML engineers often start around $70k–$110k in many tech hubs, while experienced ML engineers and research roles can reach $150k+. Freelance gigs let you earn while building real-world results; try short consulting projects like data cleanup or simple model proofs. Specialize where demand is rising — computer vision, NLP, or recommender systems — and keep learning: new models and tools arrive fast, so routine study saves time later.

Start small, ship something, then iterate every week. You'll get there.