Senior Backend Engineer with MLOps
At a glance
Senior ML Platform & Ops role at parser. Madrid · full-time.
Pay not stated ML Platform & Ops salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build infrastructure for training, deploying and serving ML models in batch and real-time settings
- Create data pipelines that process large volumes and support feature engineering
- Improve compute use and model-serving speed across machine learning systems
- Set up monitoring, logs, alerts and CI/CD practices for ML operations
- Work with data scientists to move models into production and share technical approaches with stakeholders
What you bring
- A bachelor's or master's degree in computer science, software engineering or another related technical field
- Java proficiency, with Python as a useful additional language
- A background in software, data or ML engineering, with six to seven years preferred
- Knowledge of ML methods and experience creating dependable, scalable ML systems
Who this fits
This role suits a senior engineer grounded in software, data engineering, distributed systems or applied machine learning, and ready to broaden into other areas. You’ll work hybrid from Madrid, on-site two to three days per week.
From the employer
About Parser
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.
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.
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.
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.
Location:
Madrid: Hybrid model, 2-3 days per week
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.