Senior Backend Engineer (MLOps)

Posted Yesterday
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Madrid, Comunidad de Madrid, ESP
Hybrid
Senior level
Artificial Intelligence • Software
The Role
Designs and maintains scalable MLOps infrastructure for aircraft maintenance machine-learning systems. Responsibilities include building ETL/ELT pipelines, deploying and serving models, optimizing compute resources, implementing monitoring and CI/CD, and collaborating with data scientists and maintenance engineers. The role supports predictive maintenance, fault detection, anomaly detection, and component lifecycle optimization while mentoring engineers and integrating technologies across distributed systems.
Summary Generated by Built In

Senior Backend Engineer with MLOps

At Parser, we are seeking a Senior Backend Engineer with strong MLOps expertise to join the Engineering team.

In this role, you will help design, build, and maintain the infrastructure that powers ML solutions within the Aircraft Maintenance division—managing data pipelines, streamlining model deployment, and optimizing compute resources.

You'll work on high-impact systems such as predictive maintenance, fault detection, and component lifecycle optimization, while collaborating closely with data scientists and engineering teams to integrate models into production.

Role Overview

We're seeking a passionate and experienced Software Engineer to join the Aircraft Maintenance ML team at our client. This high-impact role combines infrastructure expertise with applied MLOps knowledge, requiring proficiency in software 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.

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 of an amazing, multicultural community of tech experts.
  • A highly competitive compensation package.
  • Medical insurance.

How to Apply

If you believe you can do the job and are excited to grow with us, we'd love to hear from you. Please submit your application through our careers page.

Come and join our #ParserCommunity. 
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Skills Required

  • Bachelor's or Master's degree in Computer Science, Software Engineering, or a related technical field
  • Strong problem-solving skills
  • Proficiency in Python
  • Understanding of machine-learning algorithms and model architectures
  • Experience building scalable and reliable machine-learning systems
  • 6–7 years of experience in software, data, or ML engineering roles
  • Exposure to AWS or other cloud platforms
  • Experience with Docker and scalable data systems such as Spark and Kafka
  • Familiarity with CI/CD tools such as GitHub Actions
  • Familiarity with ML model-serving technologies such as MLflow
  • Ability to collaborate across multidisciplinary teams
  • Willingness to travel to the UK two to three times per month
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The Company
HQ: London
174 Employees
Year Founded: 2018

What We Do

At Parser we help organisations leverage on digital to advance their business strategy. For us, creativity, talent, and passion are indispensable to succeed. This is why we are committed to building, nurturing, and empowering a diverse, multi-disciplinary environment, where every team member can grow professionally and achieve their ambitions, while helping our clients deliver amazing digital products. Thanks to our flexible, distributed model of global operation and the high calibre of our experts, we have enjoyed triple digit growth over the past five years, creating amazing career opportunities for our people across 4 continents. Our services: - Digital Strategy & Transformation We help you understand market trends, relevant technologies, and aligned with your business strategy we create your vision and set the digital direction. This enables you to focus efforts and integrate business and technology across your organisation. - Digital Portfolio Management Based on a clear vision and direction, we help you assess your position, execute your digital strategy and manage your portfolio. We do this by integrating a range of diverse business and technical practises, transforming your organisation in the process. - Product & Service Development We help you create new experiences by designing user journeys that bridge all of the relevant departments of your organisation. By harnessing our ecosystem of specialised talent, we can provide relevant and focused expertise on demand.

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