AI role · Open offer

ML Engineer — remote Tunisia

InstaDeep

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At a glance

How you'll work, where, and key listing dates.

Employment: Full-time
Workplace: Remote
Location: Remote · Tunisia
Compensation: EUR 35k–52k/year + bonus
Experience level: Senior
Team: Applied research
Benefits & perks

About this role

What the team is looking for and how this position fits.

InstaDeep is hiring a Senior ML Engineer — remote Tunisia on the Applied research team (Remote · Remote, Tunisia). This is a hands-on role in LLM & generative AI—not a generic “AI enthusiast” post.

You will own LLM features, retrieval, agents, and evaluation end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.

InstaDeep offers remote ML roles based in Tunisia — decision AI with global clients.

Productize research into client systems with reproducible evals and GPU discipline.

Missions

Ship DL/LLM features from notebook to monitored production.
Document benchmarks and rollback plans for enterprise handover.

Stack & environment

Python, PyTorch, Kubernetes, AWS.

What we look for

Production ML; remote Tunisia eligibility.

Skills we're hiring for

What each capability means for this role—not a keyword checklist.

Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
LangChain
Orchestrating LLM chains, tools, and retrieval with observable steps and failure handling.
PyTorch

Tools & stack

Platforms and products mentioned for this role (APIs, IDEs, vector DBs, CRMs, design tools, etc.).

KubernetesTool
Deploying and scaling model services with health checks, autoscaling, and rollout discipline.
AWSTool
Regular use of AWS in the LLM & generative AI stack—configuration, debugging, and team conventions.

You're a strong fit if…

Tailored to this listing—use it as a self-check before you apply.

You can show evidence in your application: Production DL/LLM with reproducible evals and client handover.
Several of these show up in recent shipped work—not only on your CV: Python, LangChain, PyTorch, Docker, and Kubernetes.
You have owned LLM features, retrieval, agents, and evaluation in production: debugging live issues, running postmortems, and iterating from real user or business signals.
You communicate clearly in writing, document decisions, and can overlap with the team’s core hours for pairing and incident response.
You explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords.

Apply

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What the employer asked for

Production DL/LLM with reproducible evals and client handover.

Register, add a headline and bio, then you can attach a CV link or PDF on My account before you apply. That keeps applications tied to your profile and makes it easier for employers to review your background.

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