Machine Learning Engineer

Sageis a global academic publisher of books, journals, and library resources with a growing range of technologies to enable discovery, access, and engagement. Believing that research and education are critical in shaping society, Sara Miller McCune founded Sage in 1965. Today, we are controlled by a group of trustees charged with maintaining our independence and mission indefinitely.

Sageis a global academic publisher of books, journals, and library resources with a growing range of technologies to enable discovery, access, and engagement. Believing that research and education are critical in shaping society, Sara Miller McCune founded Sage in 1965. Today, we are controlled by a group of trustees charged with maintaining our independence and mission indefinitely.

Our guaranteed independence means we’re free to:

• Do more– supporting an equitable academic future, furthering disciplines that drive social change, and helping social and behavioural science make an impact
• Work together– building lasting relationships, championing diverse perspectives, and co-creating resources to transform teaching and learning
• Think long-term– experimenting, taking risks, and investing in new ideas

Sage Publishing seeks a self-motivated Machine Learning Engineer with a focus on MLOps (Machine Learning Operations), NLP (Natural Language Processing), knowledge graphs, and Azure services. This role involves designing, developing, and deploying Data Science & AI/ML (Artificial Intelligence/Machine Learning) solutions, products, and engineering infrastructure as part of a multi-functional team, working as a self-starter in a greenfield environment, and managing multiple deliverables to deliver high-quality results that drive business outcomes.

• Design, implement, and evolve MLOps principles, standards, frameworks, and engineering platforms to support enterprise data science & AI/ML initiatives
• Work multi-functionally to develop production data science and AI/ML solutions, with a focus on NLP, recommender systems, knowledge graphs, that address complex business needs
• Implement MLOps practices to streamline the model lifecycle from development to deployment and monitoring, with a focus on auditability, versioning, data security
• Monitor and analyze the performance of deployed models and systems, and implement optimizations as required.
• Act as point of escalation for ML Engineering related issues with deployed solutions
• Remain up-to-date with the latest trends in AI/ML, MLOps, and Azure services and apply them to enhance existing solutions
• Build relationships across the department and the wider Company to be viewed as a trusted advisor on matters of MLOps Engineering for deliverables and initiatives from departments across the business
• Coach and guide more junior members of the team / team members with other technical backgrounds
• Communicate effectively about MLOps and existing solutions with technical and non-technical audiences

• Demonstrated experience of designing and implementing MLOps principles, standards, frameworks, guidelines, and engineering infrastructure
• Demonstrated experience of working multi-functionally to deliver scalable production solutions using data science and AI/ML tools and techniques, and Azure services
• Strong applied understanding of NLP, recommender systems, and knowledge graphs
• Experience with Azure Dev Ops or similar CI/CD/CT pipelines for deploying AI/ML solutions
• Strong understanding of data engineering principles and ETL pipelines
• Proficiency in programming languages such as Python, R, Java, C#
• Familiarity with cloud-based data storage technologies like Azure Data Lake and Azure SQL Database
• Excellent…