Machine learning engineer in MLOps

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 DevOps 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 relationship building skills with the ability to gain trust and influence at senior level.
• Experience of coaching/mentoring more junior engineers or others with different technical backgrounds
• Excellent problem-solving abilities and strong communication skills

• 25 days annual leave (plus bank holidays) with 2 additional floating personal days.
• Hybrid working up to 2 days per week in office and a home working allowance.
• Life assurance, income protection, and travel insurance.
• Private medical cover and access to the company doctor.
• Tuition scheme and support for pursuing professional qualifications, including access to Sage books and journals.
• Variety of snacks and beverages available in the office.
• Healthy lifestyle reimbursement and anniversary trips.
• Access to Company loans (season ticket loan, rental deposit loan, cycle loan).