MLOps Engineer

Kapsch Group; Madrid; Híbrido; EUR 30.000 - 50.000; Jornada completa

Descripción de la vacante

Una empresa de tecnología global busca un Ingeniero MLOps para diseñar y mantener pipelines de machine learning. Se requiere experiencia en MLOps y herramientas como MLflow, Airflow, Kubernetes y Docker. El papel incluye la automatización de flujos de trabajo y la colaboración técnica con otros equipos. Ofrecen un modelo de trabajo híbrido, flexibilidad horaria y 30 días de vacaciones al año.

Servicios

Horario flexible

Modelo híbrido de trabajo (3 días remotos/semana)

30 días laborables de vacaciones

Plan de remuneración flexible

Formación

• Experiencia comprobada en ingeniería MLOps o sistemas de ML en producción.
• Sólido trasfondo en ingeniería de ML.
• Experiencia práctica con tecnologías de nube y MLOps.

Responsabilidades

• Diseñar, construir y mantener pipelines de ML de extremo a extremo.
• Implementar prácticas de MLOps.
• Colaborar con equipos de datos y productos para llevar modelos a producción.

Conocimientos

Experiencia con MLflow

Experiencia con Airflow

Proficiencia en Python

SQL

Conocimientos de Kubernetes

Docker

CI/CD con GitLab

Capacidad de trabajar en Agile

Educación

Grado en Ciencias de la Computación

Grado en Ciencia de Datos

Herramientas

Prometheus

Grafana

ClickHouse

PostgreSQL

Kafka

MinIO

Descripción del empleo

Kapsch is one of Austria's most successful global technology companies. With its comprehensive ITS (Intelligent Transportation Systems) portfolio, Kapsch is actively addressing the challenges of the present and the future with intelligent mobility solutions in a wide range of application areas. As a family-owned company founded in 1892 and headquartered in Vienna, Kapsch can look back on 130 years of experience with the future.

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We are looking for a MLOps Engineer with a strong software engineering background and hands‑on experience building and deploying end‑to‑end machine learning solutions, from experimentation to production. You will work closely with product and infrastructure teams to ensure our ML systems are scalable, reliable, and maintainable throughout their full lifecycle.

Your responsibilities

• Design, build, and maintain end‑to‑end machine learning pipelines, from experimentation to production.
• Implement and continuously improve MLOps practices to enable reproducible, scalable, and reliable ML deployments.
• Collaborate closely with data, product, and infrastructure teams to bring ML models into production efficiently.
• Automate model training, validation, deployment, and monitoring workflows.
• Build and maintain CI/CD pipelines tailored for ML systems.
• Monitor ML models and platforms in production, ensuring performance, quality, and stability.
• Contribute to the definition of technical standards, best practices, and ML platform architecture.

Your profile

• Proven experience as an MLOps Engineer, ML Engineer, or a similar role focused on production ML systems.
• Strong background in ML engineering and tools such as MLflow and Airflow.
• Hands‑on experience with cloud and MLOps technologies, including: Azure, Kubernetes and Docker, GitLab CI/CD, Helm/Helmfile
• Proficiency in Python and experience working with SQL.
• Experience with monitoring and observability stacks (Prometheus, Grafana) and log management tools (e.g., Loki).
• Degree in Computer Science, Data Science, or a related field.
• Strong English communication skills, both written and spoken.
• Analytical mindset, fast learner, collaborative attitude, and customer‑oriented approach.
• Experience working in Agile environments.

Nice‑to‑have

• Hands‑on experience with machine learning libraries such as scikit‑learn, TensorFlow, or similar.
• Knowledge of data architectures, including experience with medallion architecture.
• Experience with databases like ClickHouse and PostgreSQL.
• Familiarity with message brokers such as Kafka or Apache Pulsar.
• Experience with object storage solutions like MinIO or other S3‑compatible storages. xsgfvud
• A Master’s degree (ISCED/CINE level 7) in a relevant field.

Our offer to you:

• Permanent role
• Flexible working hours
• Hybrid working model (3 days of remote work/week)
• 30 business days of annual leave
• Flexible remuneration plan