Companies now want more than just people who can train models. They need engineers who can build, deploy, scale, and maintain them. If you want to stand out, this article will walk you through 10 projects for ML engineers in 2026. These projects will show you have the practical skills and architectural know-how that employers look for in technical interviews.
10 Projects for ML Engineers
These 10 projects help connect what you learn in school with what you need to do on the job. They start with basic deployments and move up to advanced, production-level multimodal systems.
These advanced systems are where ML meets GenAI. If you want to learn that transition, I’ve broken it down in my book Hands-On GenAI, LLMs & AI Agents.
- Real-Time Streaming Analytics using Kafka
- YouTube Video Chaptering (YouTube API + NLP)
- Music Recommendation System Using Spotify API
- AI System to Summarize YouTube Videos into Notes
- Deploy a Machine Learning Model with Docker
- Deploy Your First ML Model as a REST API
- Build a Production-Ready LLM API
- Build a Visual Question Answering App
- Building Synthetic Medical Records using GANs
- Building a Multimodal AI Model
Closing Thoughts
You don’t have to complete all ten projects right away. Choose two or three that really interest you and focus on building them thoroughly.
Write clean code, document your project’s architecture on GitHub, and be prepared to talk about your design choices in interviews.
I hope you found these 10 projects for ML engineers in 2026 helpful.
For more tips on AI and machine learning, you can follow me on Instagram. If you’re new to machine learning, check out my book to help you get started.
