MLOps Engineer
Location: Barcelona (Hybrid)
Contract: Fixed-term until June 2026
Salary: €55,000 base pro rata
Bonuses: €3,000 sign-on + €500/month retention bonus
Relocation: €2,000 package available
The opportunity
We're hiring an MLOps Engineer to join a fast-scaling European deep-tech company working at the forefront of AI model efficiency and deployment.
This team is solving a very real problem: how to take large, cutting-edge language models and run them reliably, efficiently, and cost-effectively in production. Their technology is already live with major enterprise customers and is reshaping how AI systems are deployed at scale.
This is a hands-on engineering role with real ownership. You'll sit close to both research and production, helping turn advanced ML into systems that actually work in the real world.
What you'll be working on
- Building and operating end-to-end ML and LLM pipelines, from data ingestion and training through to deployment and monitoring
- Deploying production-grade AI systems for large enterprise customers
- Designing robust automation using CI/CD, GitOps, Docker, and Kubernetes
- Monitoring model performance, drift, latency, and cost, and improving reliability over time
- Working with distributed training and serving setups, including model and data parallelism
- Collaborating closely with ML researchers, product teams, and DevOps engineers to optimise performance and infrastructure usage
- Managing and scaling cloud infrastructure (primarily Azure, with some AWS exposure)
Tech you'll be exposed to
- Python for ML and backend systems
- Cloud platforms: Azure (AKS, ML services, CycleCloud, Managed Lustre), plus AWS
- Containerisation and orchestration: Docker, Kubernetes
- Automation and DevOps: CI/CD pipelines, GitOps
- Distributed ML tooling: Ray, DeepSpeed, FSDP, Megatron-LM
- Large language models such as GPT-style models, Llama, Mistral, and similar
What they're looking for
- 3+ years' experience in MLOps, ML engineering, or LLM-focused roles
- Strong experience running ML workloads in public cloud environments
- Hands-on background with production ML pipelines and monitoring
- Solid understanding of distributed training, parallelism, and optimisation
- Comfortable working across infrastructure, ML, and engineering teams
- Strong English communication skills;
Spanish is a plusbut not required
Nice to have
- Experience with mixture-of-experts models
- LLM observability, inference optimisation, or API management
- Exposure to hybrid or multi-cloud environments
- Real-time or streaming ML systems
Why this role stands out
- Work on AI systems that are already in production with integral customers
- Tackle real infrastructure and scaling challenges, not toy problems
- Competitive salary plus meaningful bonuses
- Hybrid setup in Spain with relocation support
- Join a well-funded, high-growth deep-tech environment with long-term impact