Senior Data & AI Engineer - ML Platform

• Architect and evolve batch and near-real-time data pipelines supporting ML workloads.
• Build scalable processing pipelines with Spark.
• Design orchestration using Airflow.
• Work with high-volume event streams, including Kafka.
• Design and evolve feature pipelines and a central feature store.
• Improve the path from experimentation to production: training, validation, versioning and deployment.
• Build reliable interfaces between data infrastructure and online model services.
• Define data and model observability across freshness, quality, latency, drift and business metrics.
• Improve CI/CD, automated testing and infrastructure deployment.
• Diagnose performance bottlenecks across compute, storage, network and model-serving workloads.
• Make sensible trade-offs between latency, reliability, cloud cost and engineering complexity.
• Partner with ML Engineers and Data Scientists without throwing notebooks over the wall.
• Mentor other engineers and raise engineering standards through design reviews and code reviews.

Languages: Python, SQL; some Scala/Java Data: Spark, Airflow, Kafka ML platform: MLflow, model registry, feature-store patterns Cloud: AWS Infrastructure: Kubernetes, Terraform, CI/CD Observability: metrics, tracing, alerting, data/model monitoring

You don’t need previous experience with every component. Strong fundamentals matter more than matching an exact tool list., * Around 5+ years building production data or ML infrastructure.

• Strong Python engineering skills.
• Experience with distributed data processing.
• Good understanding of data modelling and feature engineering.
• Hands-on production experience with orchestration.
• Familiarity with the lifecycle of an ML model beyond training: versioning, deployment, monitoring and rollback.
• Experience debugging systems under real production constraints.
• Confidence making architectural decisions and explaining the trade-offs behind them.
• An ownership mindset: reliability and maintainability remain your problem after deployment.
• Professional English., Experience with feature stores, Kafka at scale, Kubernetes, online model serving, low-latency systems, deep learning infrastructure or cost optimisation of large cloud data workloads., Past system + production ML architecture problem.

1. Engineering leadership conversation: 60 min Technical judgement, collaboration, ownership and mutual expectations.

2. Then decision and offer.

No week-long process and no artificial algorithm puzzles.

• €58k-72k base salary.
• Performance bonus on top.
• Permanent contract.
• Remote work anywhere in Spain.
• Flexible working hours.
• Private medical insurance.
• Meal / flexible compensation package.
• Individual learning budget.
• 25+ days’ annual leave.
• Regular engineering off-sites.
• A senior IC path - becoming a manager is not the only way to progress.

You’ll have substantial technical ownership without being asked to become a project coordinator who no longer writes code.

Client: European Real-Time Decisioning Product Company

If you enjoy the point where Data Engineering, distributed systems and Machine Learning meet, this is that job.

Our client is a European product company building a real-time decisioning platform used by digital businesses across multiple markets.

Its technology processes high volumes of behavioural events and turns them into predictions used by production systems in milliseconds: which users are likely to churn, which actions are relevant, and where the platform should allocate resources.

Machine Learning isn’t an innovation project sitting on the side of the business. It’s part of the product.

The next challenge is making the ML ecosystem easier to scale, safer to change and cheaper to run.