Hi, I'm Alassane

ML / AI Engineer
who builds and ships AI systems end-to-end

I build recommendation engines, computer vision applications, LLM pipelines, and scalable data infrastructure — taking projects from research to production.

Currently engineering ML systems — recommendation engines, computer vision, and LLM pipelines in production.

Expertise

AI / ML Engineering

LLMs (Claude, OpenAI, Gemini), RAG pipelines, recommendation systems, computer vision, NLP, embeddings, similarity search, and finetuning.

Production ML & Deployment

End-to-end ML lifecycle — from data pipelines and model training to real-time inference, API deployment, and monitoring.
GCP, Docker, REST APIs, CI/CD.

Data & Infrastructure

Snowflake, dbt, Snowflake Cortex, GCP, semantic layer design, data modeling, CI/CD.

👉 Explore a selection of my projects

I worked with special people

"Working with Alassane was a transformative experience. His expertise in AI & Data science seamlessly merged with our passion for the product we design. Alassane is a valuable collaborator, bringing innovation and dedication to every project.
What I value the most about working with Alassane is his exceptional reliability and professionalism. His methodical approach to problem-solving has consistently proven to be a perfect match for our company."
— Elise Tastet, Founder & CEO of Tastet.ca

Context

Elevating a culinary platform with AI-driven recommendations
I collaborated with Tastet, a local gourmet guide focusing on equity, diversity, and personalization.
Together, we worked on creating an innovative approach to culinary exploration.

Outcome

In our collaboration, we designed a comprehensive tech stack, orchestrating a seamless end-to-end pipeline for AI-driven recommendations. From data collection in a Google Storage bucket to a Docker container hosting a real-time recommendation API, every step was meticulously crafted for optimal performance.

👉 Learn more about the Tastet Project

How I Work

Ship It: I focus on getting AI systems into production — not just building prototypes. Research matters, but deployed systems matter more.
End-to-End Ownership: From data pipelines and model development to API deployment and monitoring, I own the full lifecycle of ML projects.
Iterative Engineering: I build in tight feedback loops — ship early, measure, improve. Continuous iteration leads to better systems.
Depth Where It Counts: I specialize in recommendation systems and ranking models, and I know when to reach for classical ML, deep learning, or LLMs to solve the problem at hand.