Senior Backend Engineer with MLOps

About the role

At Parser, we are seeking a Senior Backend Engineer with strong MLOps expertise to join the Machine Learning Engineering team of a global leader in the travel industry. In this role, you will help design, build, and maintain the infrastructure that powers our ML solutions-managing data pipelines, streamlining model deployment, and optimizing compute resources. You'll work on high-impact systems such as ranking, recommendation, and pricing optimization, while collaborating closely with data scientists to integrate models into production.

Role Overview

We're seeking a passionate and experienced SW Engineer to join the Revenue Optimization ML team. This high-impact role combines infrastructure expertise with applied MLOps knowledge, requiring proficiency in software engineering, distributed systems, and machine learning operations.

What you'll do

Key Responsibilities

• 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, and contribute to ML Ops practices including CI/CD pipelines.
• Collaborate closely with Data scientists to streamline the model development-to-production workflow.
• Research and integrate new technologies, mentor junior engineers, and communicate technical solutions to diverse stakeholders.

What we're looking for

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 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), and ability to collaborate well across teams.

Nice to have

Ideal Candidate

This role is well-suited to someone with a solid foundation in at least one of the following areas: software engineering, data engineering, distributed systems, or applied machine learning and a readiness to grow into others as the work requires. We welcome candidates who are strong, language-agnostic problem solvers and passionate about building robust ML systems, even if you don't meet every single requirement listed.

What you'll get

What We Offer

• A friendly and collaborative team keen on knowledge sharing and continuous learning
• Health Insurance
• Opportunity to work on cutting-edge MLOps & Machine Learning infrastructure, leveraging technologies like Java, Python, AWS, Docker, Kafka, Spark, and MLflow to power real-time ML systems.

How you'll work

Location:

Madrid: Hybrid model, 2-3 days per week

About the company & team

About Parser