Mid Machine Learning Engineer

Powertalent; Setúbal; Híbrido; EUR 52 000 - 76 000; Tempo integral

Resumo da oferta

Powertalent is seeking a Mid Machine Learning Engineer to bring ML models into production, ensuring reliability and efficiency. You will collaborate with Data Scientists and Data Engineers to move models from development to real-world production environments.

As we build our MLOps practice from the ground up, you will shape how we deploy, monitor and manage ML models. This hands-on role offers ownership and the chance to build solutions from scratch.

Qualificações

• 3+ years of experience in Machine Learning.
• Strong Python skills.
• Good SQL knowledge.
• Experience with at least one cloud platform: AWS, GCP or Azure.
• Experience with CI/CD, such as GitHub Actions or Jenkins.
• Good understanding of MLOps fundamentals, including Git, pull requests, code reviews and versioning.
• Experience with ML frameworks such as TensorFlow, PyTorch or Scikit-learn.
• Experience deploying models using Docker and/or Kubernetes.
• Experience building APIs with FastAPI or Flask.
• Understanding of testing practices and frameworks such as pytest or unittest.
• Ability to work closely with technical teams and communicate clearly.
• Proactive mindset, ownership and willingness to learn.
• English B2 or higher.

Responsabilidades

• Deploy and manage Machine Learning models in production.
• Build and maintain CI/CD pipelines for ML workloads.
• Monitor model performance, failures and key metrics.
• Implement monitoring and basic alerting solutions.
• Write clean, maintainable and testable Python code.
• Work closely with Data Scientists and Data Engineers.
• Improve model performance and production response times.
• Help automate model retraining and deployment processes.
• Contribute to the development of our MLOps practices and infrastructure.

Conhecimentos

Python

SQL

MLOps basics

Git workflows

Cloud platforms

Model deployment

APIs (FastAPI/Flask)

Ferramentas

Docker

Kubernetes

GitHub Actions

Jenkins

FastAPI/Flask

PyTest/Unittest

TensorFlow

PyTorch

Scikit-learn

Descrição da oferta de emprego

We're looking for a Mid Machine Learning Engineer to help bring Machine Learning models into production and make sure they run reliably and efficiently.

You'll work closely with Data Scientists and Data Engineers, taking models from development into real-world production environments.

As we're building our MLOps practice from the ground up, you'll have the opportunity to help shape how we deploy, monitor and manage ML models. This is a hands-on role where you'll have real ownership and the chance to build solutions from scratch.

What You'll Do

• Deploy and manage Machine Learning models in production.
• Build and maintain CI/CD pipelines for ML workloads.
• Monitor model performance, failures and key metrics.
• Implement monitoring and basic alerting solutions.
• Write clean, maintainable and testable Python code.
• Work closely with Data Scientists and Data Engineers.
• Improve model performance and production response times.
• Help automate model retraining and deployment processes.
• Contribute to the development of our MLOps practices and infrastructure.

What We're Looking For

• 3+ years of experience in Machine Learning.
• Strong Python skills.
• Good SQL knowledge.
• Experience with at least one cloud platform: AWS, GCP or Azure.
• Experience with CI/CD, such as GitHub Actions or Jenkins.
• Good understanding of MLOps fundamentals, including Git, pull requests, code reviews and versioning.
• Experience with ML frameworks such as TensorFlow, PyTorch or Scikit-learn.
• Experience deploying models using Docker and/or Kubernetes.
• Experience building APIs with FastAPI or Flask.
• Understanding of testing practices and frameworks such as pytest or unittest.
• Ability to work closely with technical teams and communicate clearly.
• Proactive mindset, ownership and willingness to learn.
• English B2 or higher.

Nice to Have

• Knowledge of Java or Scala.
• Experience with model optimization or compression.
• Previous experience working with MLOps platforms or production ML environments.

Work model: 2 days per week in the office (in Lisbon) and 3 days at home.