Machine Learning Engineer

Work setup:

Hybrid in Lisbon, 2 days per week in the office.

About Loop Future

Loop Future combines technology, innovation, and sustainability to create digital solutions that shape the future. By integrating software engineering, cloud solutions, salesforce expertise, and intelligent sourcing, Loop Future connects people, systems, and industries — driving efficiency, growth, and global impact. We are Headquartered in Portugal but we are also supported by our international offices in the UK, Switzerland and India. The company brings together a diverse and talented team committed to delivering cutting-edge digital experiences into domains such as TV & Media, Satellite & Space, Consultancy, Retail and many other domains.

About Role

For an international Tech Hub emerging from Lisbon, we're looking for a Mid Machine Learning Engineer to help bring machine learning solutions into production, ensuring reliability, monitoring, documentation, and close collaboration with our data science team. As we build our MLOps practice from the ground up, you'll play a key role in turning models into scalable, production-ready systems.

This is a hands-on, builder-focused role. We don't yet have sophisticated MLOps infrastructure in place, so you'll help create solutions from scratch, including manual model deployments before automation is introduced. We value people who understand when a pragmatic solution is the right one, work effectively without heavy processes, and take ownership of what they build.

Responsibilities

Monitoring and alerting

Production performance optimization

Retraining automation initiatives

Productionize ML models - Reliably deploy models developed by data scientists into production environments.

Build and maintain deployment pipelines — Develop and support ML-focused CI/CD pipelines.

Monitor models in production - Track performance, failures, and key metrics, implementing basic alerting where needed.

Write quality, maintainable code - Produce clean, structured, testable Python code aligned with team standards.

Collaborate cross-functionally - Work closely with data scientists and data engineers on model deployment and data transformation initiatives.

Optimize performance - Improve model response times and operational efficiency in production environments.

Requirements

Familiarity with AWS, GCP, or Azure for running and integrating ML workloads

Experience building and maintaining CI/CD pipelines using tools such as GitHub Actions or Jenkins

Strong Python skills, including project structuring, reusable and testable code, object-oriented programming, design patterns, and testing frameworks (pytest/unittest)

Strong SQL skills for querying and manipulating data in support of model development

Understanding of MLOps fundamentals, including Git versioning, pull requests, code reviews, and ML-specific versioning practices

Experience with core ML frameworks, including TensorFlow, PyTorch, and Scikit-learn

Experience deploying models with Docker or Kubernetes, including an understanding of model optimization and compression

Familiarity with building model-serving APIs using FastAPI or Flask

Nice to have: Basic knowledge of Java or Scala for integrating with existing systems

Strong collaborator who works effectively with data scientists and engineers to align models, data, and operations

Proactive communicator who clearly shares progress, blockers, and support needs

Solid problem-solver capable of handling moderately complex technical challenges with appropriate guidance

Takes ownership and accountability for operational deliverables, including pipelines, deployments, and production fixes

Learns quickly and adapts based on technical feedback and evolving best practices

More than 3 years of experience in the Machine Learning field.

English proficiency should be at least B2 level.

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