Hire the AI Engineer who ships your models to production.
From LLM prototype to production system: we recruit AI engineers who know how to industrialize, monitor and secure your models — not just run them in a demo.
The role
The AI Engineer takes over where the prototype ends: they turn a notebook or a POC into a reliable, monitored, budgeted system. They design RAG pipelines, integrate LLMs into an existing product, handle fine-tuning when it's justified, and monitor latency, inference cost and response drift in production.
It's a young, highly sought-after role, which makes it easy to hire badly for. Many candidates present an 'AI' background built on a hackathon or a ChatGPT wrapper, without ever having run a system under real load. Without a technical screening framework, selection ends up based on pitch rather than proof — and the mistake is costly: a rare profile, a high salary, a strategic project delayed by months.
The right profile
- Real experience shipping models or LLM systems to production (MLOps/LLMOps) — not just notebook prototyping.
- Hands-on command of RAG, prompt engineering and LLM API integration (OpenAI, Anthropic, Mistral, open-source models).
- Solid software engineering foundations — Python, APIs, cloud architecture, CI/CD — at least as strong as their pure machine learning skills.
- A sense for cost and latency: knows how to trade off model performance against the product's economic viability.
- Autonomy and clarity: able to explain a technical AI decision to a non-tech board, and to say no to a use case that doesn't hold up.
Salary range
| Seniority | Base | Variable | OTE |
|---|---|---|---|
| Junior | 40–48 k€ | 0–4 k€ | 40–52 k€ |
| Mid-level | 55–68 k€ | 3–9 k€ | 58–77 k€ |
| Senior | 75–95 k€ | 5–15 k€ | 80–110 k€ |
How we recruit
We start with your context.
An in-depth call to understand culture, commercial challenges, and non-negotiable criteria. We never start blind.
Active identification.
Our in-house agents + Apify, Kaspr, lemlist and Waalaxy to identify and reach out. Claude and Mistral to analyze and summarize. Fireflies and Noota to capture interviews. LinkedIn Recruiter for search.
5 to 10 aligned profiles.
Every profile presented has been met by Walid in interview. Each with a detailed brief, through to decision, negotiation and onboarding.
Frequently asked questions
What's the difference between an AI Engineer and a Data Scientist?
The Data Scientist explores and proves the value of a use case; the AI Engineer makes it hold up in production — APIs, monitoring, costs, security. At a start-up, one person often covers both roles: we scope the exact boundaries with you before starting the search.
Do you need a PhD or a research background for this role?
No, in the vast majority of cases. The role mainly calls for solid software engineering and real experience integrating models in production — a good backend developer who has upskilled on LLMs is often a better fit than a pure research profile.
How long does it take to hire an AI Engineer with Walead?
Our average timeline across all roles is under 20 days, but this profile is among the tightest on the market: expect 3 to 5 weeks for a well-scoped search instead. We'd rather give you an honest range than a generic promise.
CDI, freelance, or RPO — which format for this role?
For a first AI Engineer laying down the technical foundations, CDI protects your continuity; for a one-off project (LLM migration, a POC to industrialize), freelance is quicker to secure. If you're hiring several Tech/AI profiles over the year, an embedded RPO smooths out cost and sourcing load.
A role to fill?
First call free, no commitment. Book a slot or describe your need — we get back to you within 24 business hours.