Mid Machine Learning Engineer — Production & MLOps

Zohorecruit; Lisboa; Híbrido; EUR 65 000 - 85 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Hybrid work options

Flexible hours

Professional development opportunities

Resumo da oferta

Genesis Digital Solutions is seeking a Mid Machine Learning Engineer to productionize ML models, build deployment pipelines, and monitor systems for reliable performance. You will work with Data Scientists and Data Engineers to operationalize models, optimize deployments, and contribute to MLOps practices.

The role emphasizes collaboration, ownership of deployments, and building scalable ML workflows in a hybrid work environment.

Qualificações

• 3+ years of professional experience in Machine Learning.
• Strong Python skills with well-structured, testable code.
• Proficiency in SQL for data querying and manipulation.
• Experience building CI/CD pipelines (GitHub Actions, Jenkins).
• Foundations in MLOps, versioning, PRs, code reviews, and ML-specific versioning.

Responsabilidades

• Productionize ML models and deploy them reliably in production environments.
• Build and maintain ML-focused CI/CD and deployment pipelines.
• Manage ML models across their production lifecycle.
• Implement monitoring and basic alerting for model performance and failures.
• Optimize model response times and production performance.
• Contribute to retraining automation and ML workflow improvements.
• Develop reusable, testable Python code and APIs (FastAPI/Flask).
• Collaborate with Data Scientists and Data Engineers on deployment and data transformation.
• Deploy models with Docker and/or Kubernetes.

Conhecimentos

Python

TensorFlow

PyTorch

Scikit-learn

SQL

CI/CD

Docker

Kubernetes

FastAPI/Flask

MLOps

Cloud (AWS/GCP/Azure)

Git & code reviews

Testing (pytest/unittest)

Ferramentas

GitHub Actions

Jenkins

Descrição da oferta de emprego

Genesis Digital Solutions is seeking a Mid Machine Learning Engineer to productionize ML models, build deployment pipelines, and monitor systems for reliable performance. You will work with Data Scientists and Data Engineers to operationalize models, optimize deployments, and contribute to MLOps practices.

The role emphasizes collaboration, ownership of deployments, and building scalable ML workflows in a hybrid work environment.