Here are some ideas to get you started:
- 📫 Open to collaboration on SSL-AI research.
- 🔭 I’m currently working on Reachy Mini.
- 🌱 I’m currently learning so many things 🤣
- 🕺🏻I’m looking to collaborate on MLOPS , Gen-AI, NLP and GradientBluePrint repos.
- 📫 How to reach me: R.mohammadi@northeastern.edu
- ⚡ Fun fact: Love my 🐶 and
and 💪
✨ About Me
I’m an AI/ML engineer and technical leader with over 8 years of experience building scalable machine learning systems across domains like aerospace and defense, healthcare, cybersecurity, and enterprise automation. My work spans end-to-end ML pipelines, federated learning, NLP, MLOps, and Generative AI—from architecting infrastructure to deploying models in production environments with tight privacy and compliance constraints.
I’ve led cross-functional ML teams, developed LLM-powered automation agents, built streaming data pipelines, and helped drive AI-first product strategy through fast, high-impact iterations. My focus is always on bridging the gap between research and real-world implementation.
I also serve as an Adjunct Professor at Northeastern University, where I teach graduate and Ph.D.-level courses in Machine Learning, MLOps, NLP, and Generative AI. I lead a team of teaching assistants, mentor project-based learning at scale, and help students land roles in applied AI.
Explore my repositories to find work in MLOps, Generative AI, RAG pipelines, and NLP, and feel free to reach out if you’d like to collaborate or geek out about building real, production-ready AI systems.
🎬 [Cousera]
- IE 7300 - Statistical Learning In Engineering
- IE 7374 - Generative AI
- IE 7374 - Generative AI - Labs
- IE 7374 - Machine Learning In Operation
- IE 7374 Labs - Machine Learning In Operation - Labs
- Intel Ignite Alumni - Intel Ignite
- Finalist of the Boston Scientific Callenge at Google Final Round Video
- Akira Yamamura Excellence in Research Award - Northeastern University
- Achieved PhD Spotlight at Northeastern University - PhD Spotlight
Listen to the episode using the link above:
Listen to the episode using the link above:
