Machine Learning Engineer
1 tydzień temu
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. 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 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 3 engineers What we expect in general The ability to work in a hybrid model from the Krakow office is a must ( 1 day per week ). Hands-on experience in deploying Python projects. Strong experience in writing high-quality Python code. Experience with orchestration tools such as Airflow. Knowledge of Spark or other distributed data processing tools. Experience with Kubernetes ecosystem as a user. Strong experience in Azure and Docker Ability to work in a team and participate in the design process. Good command of English (B2/C1). Strong communicator Team player with mentoring ability Proactive and responsible Strategic thinker with big-picture perspective
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