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Join our team to drive business innovation with production-ready machine learning pipelines. You will play a key role in deploying and maintaining ML workflows, leveraging Azure for cloud computing and on-prem clusters for ETLs. Collaborating closely with Data Scientists, you will contribute to AI-powered projects while shaping the organization’s technical culture.
Forecasting and Commodities
Project scope
As an ML Engineer in Forecasting and Commodities, you will be involved in projects that support critical decision making processes, by applying your Python, PySpark, Kubernetes and Cloud (Azure) skills. You will be working in a technically mature ecosystem, implementing new features and covering new use-cases. Part of your responsibilities will be design and implementation of a data science innovation framework, as well making contributions to an overall engineering best practises of the organization.
Your key responsibilities would be:
Developing libraries, tools, and frameworks that standardise and accelerate development and deployment of machine learning models.
Working in an Azure cloud environment, developing model training code in AzureML. Building and maintaining cloud infrastructure with IaC (infrastructure as code).
Working with distributed data processing tools such as Spark, to parallelise computation for Machine Learning.
Diagnosing and resolving technical issues, ensuring availability of high-quality solutions that can be adapted and reused.
Collaborating closely with different engineering and data science teams, providing advice and technical guidance to streamline daily work.
Championing best practices in code quality, security, and scalability by leading by example.
Taking your own, informed decisions moving a business forward.
Tech stack
Python, PySpark, Airflow, Docker, Kubernetes, Azure (incl. Azure ML), pandas, scikit-learn, numpy, GitHub Actions, Azure DevOps, Terraform, Git, GitHub.
Team
3 engineers
Building tech community
Flexible hybrid work model
Home office reimbursement
Language lessons
MyBenefit points
Private healthcare
Training Package Virtusity / in-house training
And a lot more!
Join our team to drive business innovation with production-ready machine learning pipelines. You will play a key role in deploying and maintaining ML workflows, leveraging Azure for cloud computing and on-prem clusters for ETLs. Collaborating closely with Data Scientists, you will contribute to AI-powered projects while shaping the organization’s technical culture.
Forecasting and Commodities
Project scope
As an ML Engineer in Forecasting and Commodities, you will be involved in projects that support critical decision making processes, by applying your Python, PySpark, Kubernetes and Cloud (Azure) skills. You will be working in a technically mature ecosystem, implementing new features and covering new use-cases. Part of your responsibilities will be design and implementation of a data science innovation framework, as well making contributions to an overall engineering best practises of the organization.
Your key responsibilities would be:
Developing libraries, tools, and frameworks that standardise and accelerate development and deployment of machine learning models.
Working in an Azure cloud environment, developing model training code in AzureML. Building and maintaining cloud infrastructure with IaC (infrastructure as code).
Working with distributed data processing tools such as Spark, to parallelise computation for Machine Learning.
Diagnosing and resolving technical issues, ensuring availability of high-quality solutions that can be adapted and reused.
Collaborating closely with different engineering and data science teams, providing advice and technical guidance to streamline daily work.
Championing best practices in code quality, security, and scalability by leading by example.
Taking your own, informed decisions moving a business forward.
Tech stack
Python, PySpark, Airflow, Docker, Kubernetes, Azure (incl. Azure ML), pandas, scikit-learn, numpy, GitHub Actions, Azure DevOps, Terraform, Git, GitHub.
Team
3 engineers
Building tech community
Flexible hybrid work model
Home office reimbursement
Language lessons
MyBenefit points
Private healthcare
Training Package Virtusity / in-house training
And a lot more!
,[Deploing and monitor models in production , Building/maintaining dashboards for monitoring and insights , Mentoring team members (e.g., bi-weekly sessions) , Translating technical details for business stakeholders , Collaborating and consulting on team technical solutions Requirements: Python, Azure, Docker, GitHub Actions, AzureML, Web Apps, Airflow Additionally: Building tech community, Flexible hybrid work model, Home office reimbursement, Language lessons, MyBenefit points, Training Package, Virtusity / in-house training.