Backend Engineer, Python & MLOps
Comunidad de Madrid
Municipality of Madrid, Spain
yesterday
Role details
Contract type
Permanent contract Employment type
Full-time (> 32 hours) Working hours
Regular working hours Languages
English Experience level
SeniorJob location
Municipality of Madrid, Spain
Tech stack
Java
Amazon Web Services (AWS)
Continuous Integration
Data Systems
Github
High-Level Architecture
Python
Machine Learning
Software Engineering
Data Logging
Feature Engineering
Spark
Containerization
Information Technology
Kafka
Machine Learning Operations
Data Pipelines
Docker
Job description
- Help design, build, and maintain the infrastructure that powers ML solutions
- Manage data pipelines, streamline model deployment, and optimize compute resources
- Work on high-impact systems such as ranking, recommendation, and pricing optimization
- Collaborate closely with data scientists to integrate models into production
- Design and implement scalable ML infrastructure for training, deployment, and serving in batch and real-time environments
- Build and maintain efficient data pipelines for large-scale processing and feature engineering
- Optimize compute resources and improve model serving performance across ML systems
- Implement robust monitoring, logging, and alerting systems
- Contribute to ML Ops practices including CI/CD pipelines
- Research and integrate new technologies, mentor junior engineers, and communicate technical solutions to diverse stakeholders
Requirements
- Bachelor's or Master's degree in Computer Science, Software Engineering, or a related technical field
- 6-7 years of experience in Software, Data, or ML engineering roles (preferred)
- Strong problem-solving skills
- Proficiency in Java and desirable Python
- Understanding of ML algorithms, model architectures, and experience building scalable, reliable ML systems
- Exposure to cloud platforms (e.g., AWS), containerization (Docker), and scalable data systems (e.g., Spark, Kafka)
- Familiarity with CI/CD tools (e.g., GitHub Actions), ML model serving technologies (e.g., MLflow)
- Ability to collaborate well across teams