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.

Your role

As a Senior Data & AI Engineer, you'll take significant ownership of the infrastructure connecting raw events to production ML models.

This is primarily an engineering role rather than a Data Scientist position.

You'll spend your time on pipelines, orchestration, feature engineering infrastructure, production model workflows, reliability and architecture.

Expect roughly 80% hands-on engineering and 20% technical guidance and mentoring.

What you'll own

• 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.

Technical environment

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.

What we're looking for

• 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.

Strong pluses

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.

What you'll get

• €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.

Hiring process

1. Talent conversation: 45 min

2. Technical architecture interview: 60 min
Past system + production ML architecture problem.

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

4. Then decision and offer.

No week-long process and no artificial algorithm puzzles.

We hire this profile on an ongoing basis. Depending on current team needs, the process may lead to an immediate opportunity or to our priority talent network.