77 515
13 327
Available projects:
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.
Responsibilities
- 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
Project Challenges
- Building a system that provides accurate and up-to-date business forecasts, by providing a set of tools that can be easily leveraged by data scientists and analysts.
- Streamlining the process of onboarding, deployment and patching new ML pipelines.
- Collaborating with cross-functional teams enhancing customer experiences through innovative technologies.
- Employing DevOps practises for reproducible patterns in multiple business domains.
Team
5 Engineers
Project Scope
As an ML Engineer in StoreOps, you will dive into projects that streamlining retail operations through the use of analytics and ML, by applying your Python, Spark, Kubernetes, and Cloud (Azure) skills. You will be contributing to a mix of mature and new projects by bringing machine learning pipelines into production, building and maintaining robust Azure infrastructure, as well as fostering a technical culture of the organization.
Responsibilities
- Developing machine learning models and feature engineering pipelines with cooperation with data scientists.
- 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), KServe, Feathr, Dask, xgboost, pandas, scikit-learn, numpy, GitHub Actions, Azure DevOps, Terraform, Git @ GitHub
Project Challenges
- Serving machine learning models online based on an online feature store.
- Enhancing the monitoring, reliability, and stability of deployed solutions, including the development of automated testing suites.
- Automating the machine learning model lifecycle to continuously improve the performance on production.
- Collaborating with cross-functional teams enhancing customer experiences through innovative technologies.
Team
5 engineers
Don’t worry if you don’t meet all the requirements. What matters most is your passion and willingness to develop. Moreover, B2B does not have to be the only form of cooperation. Apply and find out!
Available projects:
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.
Responsibilities
- 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
Project Challenges
- Building a system that provides accurate and up-to-date business forecasts, by providing a set of tools that can be easily leveraged by data scientists and analysts.
- Streamlining the process of onboarding, deployment and patching new ML pipelines.
- Collaborating with cross-functional teams enhancing customer experiences through innovative technologies.
- Employing DevOps practises for reproducible patterns in multiple business domains.
Team
5 Engineers
Project Scope
As an ML Engineer in StoreOps, you will dive into projects that streamlining retail operations through the use of analytics and ML, by applying your Python, Spark, Kubernetes, and Cloud (Azure) skills. You will be contributing to a mix of mature and new projects by bringing machine learning pipelines into production, building and maintaining robust Azure infrastructure, as well as fostering a technical culture of the organization.
Responsibilities
- Developing machine learning models and feature engineering pipelines with cooperation with data scientists.
- 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), KServe, Feathr, Dask, xgboost, pandas, scikit-learn, numpy, GitHub Actions, Azure DevOps, Terraform, Git @ GitHub
Project Challenges
- Serving machine learning models online based on an online feature store.
- Enhancing the monitoring, reliability, and stability of deployed solutions, including the development of automated testing suites.
- Automating the machine learning model lifecycle to continuously improve the performance on production.
- Collaborating with cross-functional teams enhancing customer experiences through innovative technologies.
Team
5 engineers