Machine Learning Engineer

Transport is at the core of modern society. Imagine using your expertise to shape sustainable transport and infrastructure solutions for the future. If you seek to make a difference on a global scale, working with next-gen technologies and the sharpest collaborative teams, then we could be a perfect match. 

Who are we?

At Decision Intelligence in Volvo Production Logistics, we work in networks and self-forming teams, making decisions together in our communities where we have data and knowledge. Our cultural transformation brings joy, energy, and results. We always try new things, love teaching and coaching. We explore new technologies and ways of working, evolving every day, every week. We work for end-to-end orchestration and find ways to augment all teams on an energizing journey. Transforming is us. 

We are in the AI age, exploring new technologies and solutions with the aim of developing a future competitive edge for the Volvo Group. You will play an important role in creating the supply chain of the future.

What you will do

The supply chain business is changing, and as our Machine Learning Engineer you will play an important role in digitalization where automated supply chain business processes are transforming business and people.

In Volvo Group Production Supply Chain, you will collaboratively be part of our technical transformation journey, building needed capabilities in a more and more digitalized business. As our first spearhead in ML Engineering you will be part of designing and applying our MLOps solution and journey – bridging data science and software engineering and facilitating industrialization of exploratory ML models to production.

Among your responsibilities you’ll find:

• Drive MLOps/ModelOps architecture and concept application for Production Supply Chain
• Develop and manage automation of model deployment pipelines
• Systematic observability of models and ML pipelines
• Develop and manage model repository
• Collaborate with data scientists in model industrialization
• Support architectural readiness of explorations and technical feasibility to facilitate implementation of models in digital products.

Who are you?

You get energy from development that makes it all come together, operationalizing automated decisions  integrated into digital products in a cloud (Azure) ecosystem of micro services. You bring expertise in MLOps; standardize and automate deployment and management of ML and GenAI models, operationalization of ML pipelines, versioning and CI/CD of ML models, systematic model observability and have knowledge of concepts such as On-line training, AutoML, Model competition and Decision Intelligence. To feel at home in the current architecture you need to have extensive experience in Azure ML, Databricks, ML Flow and Azure Data factory. You have experience in deploying ML/AI-powered web application in a cloud environment.