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# Senior Data & AI Engineer - ML Platform

- **Company:** Value Crew
- **Location:** Madrid, Spain (Remote available)
- **Salary:** €58,000.0 - €72,000.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Artificial Intelligence, Airflow, Amazon Web Services, Automation of Tests, Code Review, Continuous Integration, Data Infrastructure, Software Debugging, Distributed Computing Environment, Python (Programming Language), Machine Learning, Operational Databases, Azure Machine Learning, SQL Databases, Management of Software Versions, Feature Engineering, Apache Spark, Deep Learning, Kubernetes, Low Latency, Apache Kafka, Machine Learning Operations, Terraform, Data Pipelines
- **Published:** September 14, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=b32bec0ed12154a0

## About the Role

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.

 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.

## Description

* 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.