Data & Software Engineer · MLOps

I build the part of the system nobody puts in the demo — the pipelines, the deploys, the dashboards that light up at 3am so a human doesn't have to.

Most of my work lives in the messy middle, where data, models and infrastructure have to actually run together — and keep running on a Tuesday morning, three months after launch. I did a CS engineering degree, then an MSc in AI, Data & MLOps at Télécom Paris (Institut Polytechnique de Paris), and I've spent it mostly on data-heavy systems where a wrong number or an hour of downtime isn't a rounding error.

I care less about the version that demos well, more about the version that's still standing when nobody's watching.

• Turning messy sources into data you can trust — and that complains loudly instead of failing silently
• Getting ML models out of notebooks and into services that stay versioned, reproducible and watched in prod
• Infrastructure you can mostly forget about: containers, IaC, repeatable deploys
• Wiring up observability so problems show up on a graph, not in an angry message

• Building gintou
• Going deeper on MLOps / LLMOps and platform reliability
• Open to freelance & consulting on data / ML problems

Data & ML

Cloud & DevOps

Backend

Stores & streaming

Observability

Python Certified (PCAP) · AWS Certified Cloud Practitioner · Smart Analytics, ML & AI on Google Cloud · Scrum Fundamentals (SFC)

• 🌐 malaurytoussi.cm
• LinkedIn · Twitter/X · [email protected]

🇫🇷 Français (native) · 🇬🇧 English (B2)

_{"Works on my machine" means I'm only halfway done.}