JOB DESCRIPTION:

The primary tasks for a senior level Machine Learning Engineer are:

• Develop AI/ML software products and solutions, including but not limited to explore large data set, feature engineering, data pipelines, test and evaluate model output, deploy the solution for production usage and scale out for a maintainable solution in the comprehensive fashion network of the client.
• Design, develop and maintain the large-scale data infrastructure required for the AI/ML projects.
• Leverage on understanding of software architecture and software design patterns contributing to architectural decisions, technical roadmap planning, technical documentation, and to write scalable, maintainable, well-designed, and future-proof code.
• Develop solutions, components, services and frameworks to address both specific and common needs in AI/ML projects, like feature reuse, model traceability, monitoring, A/B test, versioning, release, and serving of both online inference endpoints and batch inference.
• Work in cross-functional agile team of highly skilled engineers, data scientists, and business stakeholders - to build the AI ecosystem within the company.

Required skills and experiences:

• Have a BSc or MSc degree in Computer Science, Machine Learning or related technical field, or equivalent practical experience.
• Have 5+ years’ professional experience working in relevant role(s) for a senior level Machine Learning Engineer, ideally with experience in supply chain and demand forecasting solutions.
• A hands-on person, with great software engineering skills, who loves coding, and knows how to apply good software engineering practices, DevOps, testing and SRE principals - also for machine learning and AI projects.
• Have extensive experience in developing software products that have been successfully deployed to production and scaled in a bigger organization, also ensuring and fulfilling product quality, cybersecurity and performance requirements.
• Have several years of coding experience in modern programming languages, and strong background in Python programming, i.e. 4+ years, incl. developing back-end services.
• Have great hands-on experience with cloud technologies for cloud solutions and ML development, ideally Google Cloud Platform, and also including Vertex AI.
• Have solid experience in MLOps practices, developing ML pipelines, data pipelines and deploying ML applications to production.
• Have a foundational data science background and 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 SQL and DBT, and have a solid understanding of cloud data storage technologies and data quality.
• Have experience from aligning and analyzing business needs and requirement; driving design solutions; breaking down work into stories and tasks; communicating with stakeholders and supporting team members; and making sure organizational standards and best-practices are implemented and followed.
• Familiar with agile ways of working, team collaboration and data-driven

Martina Fritjofsson
Business Development Manager
martina@emagine-consulting.se
+4670956365

• Develop AI/ML software products and solutions, including but not limited to explore large data set, feature engineering, data pipelines, test and evaluate model output, deploy the solution for production usage and scale out for a maintainable solution in the comprehensive fashion network of the client.
• Design, develop and maintain the large-scale data infrastructure required for the AI/ML projects.
• Leverage on understanding of software architecture and software design patterns contributing to architectural decisions, technical roadmap planning, technical documentation, and to write scalable, maintainable, well-designed, and future-proof code.
• Develop solutions, components, services and frameworks to address both specific and common needs in AI/ML projects, like feature reuse, model traceability, monitoring, A/B test, versioning, release, and serving of both online inference endpoints and batch inference.
• Work in cross-functional agile team of highly skilled engineers, data scientists, and business stakeholders - to build the AI ecosystem within the company