Backend and Machine Learning Engineer

The primary tasks for a Machine Learning Engineer in MLE area are:

• Develop AI/ML software products including but not limited to explore large data set, try out new algorithms, feature engineering, test and evaluate model output, deploy the solution for production usage.

• Leverage on understanding of software architecture and software design patterns to write scalable, maintainable, well-designed, and future-proof code.

• To set the standard for a scalable and accurate capability for the ML Assistant.
• To be involved in projects with customers when relevant and contribute towards VisFlow use cases. (With/Without ML)
• To be able to train ML models on a generic level for each industry and reference flow metamodels but also at specific level for customer in a scalable manner.
• To work with establishing the centre of excellence for currently ML and later AI use cases.
• To actively support the Backend Team in development of functionalities with Python, Postgresql, Apache Jina, RDF/Knowledge Graphs and be ready to learn new technologies
• Learn Swedish (At least by 2 years timeframe) if not already proficient.

Required skills and experiences:

• Have a BSc or MSc degree in computer science, engineering or related field, or equivalent practical experience.

• Have several years’ professional experience working in relevant role(s) for a Machine Learning Engineer.

• A hands-on person, with great software engineering skills, who loves coding, and like applying software engineering practices to machine learning and AI projects.

• Have experience in developing software products that have been successfully deployed to production and scaled in a bigger organization.

• Have several years of coding experience in modern programming languages, and strong background in Python programming, i.e. atleast 3 years.

• Have great hands-on experience with cloud technologies for ML development.

• Have solid experience in MLOps practices, developing ML pipelines, data pipelines and deploying ML applications to production.

• Have a strong working knowledge of a variety of AI/ML techniques and experience working with different frameworks.

• Have experience handling and developing for high volume heterogeneous data (both batch and stream), preferably also including hands-on development in DBT, and have also a solid understanding of cloud data storage technologies and data quality.

• Have experience from aligning and analyzing requirement; driving design solutions; breaking down work into stories and tasks; and making sure organizational standards and best-practices are implemented and followed.

• Familiar with agile ways of working, team collaboration, data-driven development, reliable and responsible experimentation