Machine Learning and AI Platform Engineer (LLMs) | Barcelona | Fixed term contract until July 2026 | €50k-75k p/a | Hybrid remote WFH | Quantum Computing | Artificial Intelligence

• Strong Python engineering background with experience supporting ML systems

• Experience running production services on cloud platforms (AWS, Azure or similar)

• Familiarity with containers, orchestration and automated deployment pipelines

• Understanding of the ML lifecycle and how models behave once deployed

• Comfort working across engineering, infrastructure and data teams

Nice to have (but not essential)

• Experience with ML pipelines, experiment tracking or model serving

• Exposure to large language models, RAG systems or inference optimisation

• Observability, monitoring or platform reliability experience

• Background in data engineering, backend engineering or platform teams, You don't need to come from a role called "MLOps" though MLOps experience is highly desirable - if you've been enabling ML systems in production, we want to speak with you.

Machine Learning and AI Platform Engineer (LLMs) | Barcelona | Fixed term contract until July 2026 | €50k-75k p/a | Hybrid remote WFH | Quantum Computing | Artificial Intelligence

• Hybrid remote working 2-3 days a week in office, remote WFH the rest of the time
• Open to EU Citizens only (sponsorship not available)
• Fixed term temporary contract until 1st July 2026
• Salary c. €50k-60k p/a for a mid level hire, €65k-75k p/a for a senior
• Signing bonus, retention bonus and relocation fund available

We're working with a fast-growing deep-tech company building large-scale AI systems used by enterprise clients across multiple industries.

They're looking for a Machine Learning Platform Engineer / AI Platform Engineer to help design, deploy and operate production-grade machine learning and AI systems. This is a hands-on engineering role focused on reliability, scalability and automation rather than pure research.

What you'll be working on

• Deploying and operating machine learning and AI models in real production environments

• Building automated pipelines covering training, validation, deployment and monitoring

• Running ML workloads on cloud-native platforms using containers, orchestration and CI/CD

• Improving reliability, performance and observability of ML and AI services

• Collaborating closely with ML engineers, data scientists, backend engineers and infrastructure teams