• Automate model training, evaluation, and deployment processes in development environments.
• Build and maintain CI/CD pipelines specifically for model training and evaluation.
• Ensure proper documentation for all tools, CI/CD pipelines, containers, and reusable code to maintain clarity and ease of use for future team members.
• Document best practices and guidelines for integrating new models into the workflow.
• Support continuous model retraining and monitoring efforts in collaboration with data scientists.
• Extend and maintain available containers that are compatible with different stages of deployment (training, testing, etc.).
• Work closely with the software team responsible for putting model artifacts into the production pipeline, ensuring a smooth handoff of model outputs.
• Maintain and continuously refactor reusable training/evaluation code, ensuring modularity and scalability.
• Design and implement tools to support ML workflows, such as monitoring tools or visualization aids.

Would you continue your career in a growing international organization?

Join our partner, a Global media company as next employee as a Machine Learning Engineer!

They transformed how advertisers manage creative deployment and media sourcing for campaigns while working with top brands, agencies, and media outlets. They connect creative flow across the advertising ecosystem with over 200,000 users and nearly four million assets.

• Bachelor’s or Master’s degree in Computer Science, Engineering, or Data Science.
• 5+ years of experience in machine learning engineering.
• Proven experience in building, maintaining, and automating CI/CD pipelines for machine learning projects.
• Experience with model deployment and monitoring using AWS services.
• Familiarity with cloud-based machine learning workflows and infrastructure.
• Strong proficiency in Python, Bash, and Shell scripting for automation.
  Proficient in frameworks like TensorFlow, PyTorch, and Scikit-learn for model training and evaluation.
• Hands-on experience with AWS services: Amazon S3, Amazon SageMaker, AWS Lambda, Amazon ECS/EKS.
  Experience with Docker for the containerization of ML workloads.

Nice to Haves:

• Knowledge of AWS Step Functions for orchestrating serverless workflows.
• Familiarity with Terraform for managing AWS infrastructure as code.
• Experience with distributed training.

• Chance to join a growing organization
• Annual bonus, (equal to a 13th month’s payment)
• Cafeteria
• 2 days of home office every week
• Private health insurance
• Work in a truly international environment