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Backend · AI · Full-Stack

Saad Tachrimant

Contact

Hiring for a backend, AI/ML, or full-stack role? Let’s talk.

I’m a backend and ML systems engineer — 3+ years in Java and Spring Boot shipping production platforms, plus applied ML work on edge and time-series systems. I’m actively looking for my next role and I read and reply to every message. If you’d like my CV first, you can read it here or download the PDF.

Open to new roles Europe · Canada · US — relocation or remote Based in Morocco (CET/GMT+1)

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What I’m looking for

So you can tell quickly whether we’re a fit.

  • Real production systems

    Services with actual users and actual load, where reliability and correctness matter.

  • A team that reviews code

    Engineers who care about design discussions, tests, and clear technical writing.

  • Backend depth, with room for ML

    Java/Spring Boot or Python at the core; applied machine learning is where I want to keep growing.

  • Ownership end to end

    Involved from design through deployment and operation, not handed a ticket queue.

  • Europe, Canada or the US

    On-site with relocation, hybrid, or fully remote — all work for me.

Research

PhD — decentralized peer-to-peer learning

I’m pursuing a PhD on decentralized peer-to-peer learning for time-series modeling, focused on practical deployment constraints — edge devices, privacy, reproducibility. It’s the same ground as my published work: the THERMODSET dataset paper at ACM e-Energy 2025.

That said, research on its own isn’t the challenge I’m after. The part I keep being drawn back to is what happens after the paper — getting a model into a service people actually depend on, with the data plumbing, failure modes, and deployment discipline that takes. That’s engineering, it’s where I’ve done my strongest work, and it’s what I want to be doing full time.

So I’m looking for a role, not an arrangement built around a PhD. The research has never tied me to a place and asks nothing of an employer — I’m free to relocate for the right team, or to work fully remote.

What it brings to a team

  • • Strong evaluation discipline: baselines, backtesting, stability checks
  • • Reproducible pipelines: deterministic preprocessing, experiment tracking
  • • Distributed/edge mindset: constraints, bandwidth, failure modes
  • • Clear technical writing: specs, ADRs, decision logs