MLOps Engineer - Production ML Pipelines (Hybrid Lisbon)

Loop Future; Lisboa; Híbrido; EUR 50 000 - 75 000; Tempo integral

Resumo da oferta

Loop Future is building an international tech hub in Lisbon and seeks a Mid Machine Learning Engineer to bring ML solutions into production, focusing on reliability, monitoring, and collaboration with our data science team.

This hands-on role requires building from scratch without mature MLOps infrastructure, documenting processes, and delivering pragmatic, production-ready pipelines for model deployment in a hybrid Lisbon setting.

Qualificações

• Experience in the machine learning field for 3+ years.
• Proficiency in Python and SQL for ML workflows.
• Experience with cloud platforms (AWS/GCP/Azure) for ML workloads.
• Experience building and maintaining CI/CD pipelines (GitHub Actions, Jenkins).
• Experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn).
• Experience deploying models with Docker or Kubernetes.
• Experience building model-serving APIs (FastAPI or Flask).
• Strong collaboration with data scientists and engineers.
• Strong communication and ownership of deployments.
• English at least B2 level.

Responsabilidades

• Monitor and optimize production ML models and pipelines.
• Deploy models from data scientists into production.
• Build and maintain ML CI/CD deployment pipelines.
• Develop and support ML-focused API services (FastAPI/Flask).
• Collaborate with data scientists and data engineers on deployment.
• Ensure reliability, monitoring, and documentation of ML systems.
• Proactively communicate progress and blockers.

Conhecimentos

Cloud platforms

CI/CD pipelines

Python

SQL

MLOps basics

ML frameworks

Docker/Kubernetes

APIs (FastAPI/Flask)

English B2

3+ years ML

Collaboration

Communication

Ferramentas

GitHub Actions

Jenkins

PyTest

Docker

Kubernetes

Descrição da oferta de emprego

Loop Future is building an international tech hub in Lisbon and seeks a Mid Machine Learning Engineer to bring ML solutions into production, focusing on reliability, monitoring, and collaboration with our data science team.

This hands-on role requires building from scratch without mature MLOps infrastructure, documenting processes, and delivering pragmatic, production-ready pipelines for model deployment in a hybrid Lisbon setting.