Machine Learning Engineer - Hybrid Remote

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. &##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. 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. 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. 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. 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.. &##3 engineers Hands-on experience in deploying Python projects. Strong experience in writing high-quality Python code. Knowledge of Spark or other distributed data processing tools. Strong experience in Azure and Docker Good command of English (B2 / C1).