If you’re applying for Data Science or Machine Learning jobs these days, you’ve probably noticed things are changing. Hiring managers aren’t just looking for house price prediction models anymore, and a basic wrapper around the OpenAI API doesn’t stand out. What teams really want is proof that you can build strong, reliable AI systems. In this article, I’ll share 10 GenAI projects for Data Scientists to help you build a portfolio that gets noticed.
GenAI Projects for Data Scientists
I’ve grouped these GenAI projects to help you build your skills step by step. You’ll start with local models and move on to automation and more advanced agent systems.
Local AI & Information Extraction
- Build a Local RAG System with Open-Source LLMs
- Building a Web UI for Your Local AI Agent
- Build an AI Resume Screener with Python & Llama 3
- Build an AI System to Summarize YouTube Videos into Notes
Agentic Workflows and Production Readiness
- Agentic AI Pipeline to Automate EDA
- Build a Multi-Tool AI Agent
- Build a Production-Ready LLM API
- Build Your First Text-to-SQL App
- Build an LLM App with Reasoning Skills
- Build a Multi-Agent System With LangGraph
Closing Thoughts
Seeing a list of 10 GenAI projects can feel overwhelming. Don’t worry about building them all at once.
Choose one project from the list and build it from the ground up. If something goes wrong, check the logs to see what’s happening. Pay attention to the data going into the model and what comes out. Try changing the prompts, adjusting the temperature, and adding error handling. This hands-on approach will help you grow from a data scientist into a skilled AI systems engineer.
If you found this article helpful, you can follow me on Instagram for daily AI tips and practical resources. You may also be interested in my latest book, Hands-On GenAI, LLMs & AI Agents, a step-by-step guide to prepare you for careers in today’s AI industry.
