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# Backend Engineer

- **Company:** Parser Limited
- **Location:** Spain
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Continuous Integration, Extract Transform Load (ETL), Data Systems, Distributed Systems, Github, High-Level Architecture, Python (Programming Language), Machine Learning, Parsing, Software Engineering, Data Logging, Feature Engineering, Apache Spark, Backend, Containerization, Information Technology, Apache Kafka, Machine Learning Operations, Data Pipelines, Docker
- **Published:** July 15, 2026
- **Apply:** https://es.trabajo.org/oferta-5354-11d63bf197df729e96a7b492c7bf4aff

## About the Role

Qualifications - 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 Python. - Understanding of ML algorithms, model architectures, and experience building scalable, reliable ML systems - ideally in safety-critical or regulated industries. - 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), and ability to collaborate well across multidisciplinary teams. Location - Madrid: Spain (willingness to travel to the UK 2-3 times a month) Some of the benefits you'll enjoy working with us: - The chance to join an organization with triple-digit growth that is changing the paradigm on how software products are built. - The opportunity to form part

## Description

engineering, distributed systems, and machine learning operations. Key Responsibilities - Design and implement scalable ML infrastructure for training, deployment, and serving in batch and real-time environments, supporting aircraft maintenance workflows. - Build and maintain efficient data pipelines (ETL/ELT) for large-scale processing and feature engineering, leveraging aircraft sensor, telemetry, and maintenance records data. - Optimize compute resources and improve model serving performance across ML systems used in maintenance scheduling and anomaly detection. - Implement robust monitoring, logging, and alerting systems, and contribute to MLOps practices including CI/CD pipelines. - Collaborate closely with Data Scientists and Maintenance Engineers to streamline the model development-to-production workflow. - Research and integrate new technologies, mentor junior engineers, and communicate technical solutions to diverse stakeholders across engineering and operations teams.


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