Senior Backend Engineer - MLOps & ML Infra

Parser Limited; Madrid; On-site; EUR 80,000 - 110,000; Full time

Benefits offered by this job

Medical insurance

Competitive compensation package

Global travel opportunities (UK)

Job summary

Parser Limited is seeking a Senior Backend Engineer with strong MLOps expertise to join the Engineering team in Madrid. You will design, build, and maintain ML-powered infrastructure powering aircraft maintenance solutions, including data pipelines and model deployment.

You will work with data scientists and engineers to deploy scalable ML systems, optimize resources, and advance monitoring, CI/CD, and production reliability across ML workflows.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, or related field.
• 6–7 years of experience in Software, Data, or ML engineering roles (preferred).
• Strong Python proficiency and ML concepts.
• Experience with ML infrastructure and production systems.

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 data pipelines (ETL/ELT) for large-scale processing and feature engineering from sensor, telemetry, and maintenance data.
• Optimize compute resources and model serving performance across ML systems for maintenance scheduling and anomaly detection.
• Implement robust monitoring, logging, and alerting systems and contribute to CI/CD pipelines for MLOps.

Skills

Python

MLOps

Distributed systems

Problem solving

Education

Bachelor’s or Master’s in CS/SE

Tools

AWS

Docker

Spark

Kafka

MLflow

Job description

Parser Limited is seeking a Senior Backend Engineer with strong MLOps expertise to join the Engineering team in Madrid. You will design, build, and maintain ML-powered infrastructure powering aircraft maintenance solutions, including data pipelines and model deployment.

You will work with data scientists and engineers to deploy scalable ML systems, optimize resources, and advance monitoring, CI/CD, and production reliability across ML workflows.