Join a well-known AI compression scale-up as an MLOps Engineer to deploy, monitor, and scale LLMs for enterprise clients. The role involves improving automation, reliability, and cost efficiency across the ML lifecycle. As an engineer, you will work closely with researchers and platform engineers to bridge research and production.
Key Highlights
Key Responsibilities
Technical Skills Required
Benefits & Perks
Job Description
MLOps Engineer
Barcelona or San Sebastián, Hybrid
Fixed-term contract until 30 June 2026
€45,000-€55,000 Salary + €3,000 sign-on bonus + €500 per month retention bonus
€2,000 relocation support
EU work authorisation required
Total bonus package available over the contract: up to €5,000+ depending on start date.
You join one of Europe’s most recognised deep-tech scale-ups. Backed by major global investors and strong EU support, they have built one of the most credible AI compression products in the market.
This compression tool is already live with major enterprise clients. Now they need more engineers to help deploy, monitor and scale it properly.
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Why apply?
You will work alongside highly technical quantum and AI engineers operating at a very high level.
You will gain hands-on exposure to large-scale LLM deployment, distributed training and real-world cost optimisation.
You will have a globally recognised deep-tech brand on your CV, working on AI efficiency at scale. That combination of compression, distributed systems and enterprise deployment opens doors across AI infrastructure, LLMOps and high-performance ML environments.
You get flexible working hours. Start early, start late, structure your day how you want. Hybrid setup (3 days in office) in Barcelona or San Sebastián.
You get meaningful bonuses on top of base salary.
What you’ll actually be doing
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Helping take compressed LLMs and get them deployed, monitored and running reliably for enterprise customers.
Improving automation, reliability and cost efficiency across the ML lifecycle.
Working closely with researchers and platform engineers to bridge research and production.
What you'll need
Experience running LLMs in production.
Comfort working with the infrastructure around them, cloud, containers, CI/CD, Kubernetes, that sort of thing.
Someone who understands what it takes to keep ML systems stable, monitored and efficient once they’re live.
Apply now! Applications reviewed daily.
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