ML Engineer & MLOps: Deploy, Monitor & Optimize Models

bridge351; Lisboa; Teletrabalho; EUR 60 000 - 90 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Health and Life Insurance

Tech Visa Company

Resumo da oferta

bridge351 is seeking an experienced Machine Learning Engineer to join our team in Lisbon with a flexible work model. You will deploy, monitor and optimize ML models, while building robust CI/CD pipelines and maintaining production-grade code in Python.

The role emphasizes collaboration with data scientists and data engineers, focusing on scalable deployment, model performance, and automated retraining initiatives, with opportunities for remote work and international projects.

Qualificações

• More than 3 years of experience in the Machine Learning field.
• English proficiency at least B2 level.
• Work model: 2 days per week in the office in Lisbon.

Responsabilidades

• 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.

Conhecimentos

Cloud platforms

CI/CD pipelines

Python

SQL

MLOps basics

TensorFlow

PyTorch

Scikit-learn

Docker/Kubernetes

FastAPI/Flask

Java/Scala

Ferramentas

GitHub Actions

Jenkins

Descrição da oferta de emprego

bridge351 is seeking an experienced Machine Learning Engineer to join our team in Lisbon with a flexible work model. You will deploy, monitor and optimize ML models, while building robust CI/CD pipelines and maintaining production-grade code in Python.

The role emphasizes collaboration with data scientists and data engineers, focusing on scalable deployment, model performance, and automated retraining initiatives, with opportunities for remote work and international projects.