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