Machine learning engineer in MLOps

Sage; Greater London; Hybrid; GBP 90,000 - 125,000; Full time

Benefits offered by this job

Annual leave 25 days

Hybrid work

Private medical

Life assurance

Travel insurance

Tuition support

Office snacks

Lifestyle reimbursement

Loans program

Job summary

Sage Publishing is seeking a self-motivated Machine Learning Engineer focused on MLOps, NLP, knowledge graphs, and Azure services. You will design, develop, and deploy data science solutions in a greenfield environment as part of a cross-functional team.

You will implement MLOps pipelines, work on NLP and recommender systems, monitor models, and stay updated with AI trends. The role offers hybrid work and opportunities to influence across departments.

Qualifications

• Experience designing and implementing MLOps principles and engineering infrastructure.
• Experience delivering scalable production ML/AI solutions using data science tools and Azure services.
• Strong understanding of NLP, recommender systems, and knowledge graphs.
• Experience with CI/CD pipelines for deploying AI/ML solutions.
• Strong data engineering and ETL skills.
• Proficiency in Python, R, Java, or C#.

Responsibilities

• Design, implement, and evolve MLOps platforms and standards for enterprise AI/ML initiatives.
• Develop production data science and AI/ML solutions focusing on NLP and graphs to address business needs.
• Implement MLOps practices to streamline model lifecycle, including auditability and data security.
• Monitor deployed models and optimize performance; serve as escalation point for ML engineering issues.
• Stay updated on AI/ML trends and Azure services to enhance solutions.
• Collaborate across departments and mentor junior engineers; communicate complex concepts clearly.

Skills

MLOps

NLP

Azure

Python

R

Java

C#

Knowledge graphs

Tools

Azure DevOps

Azure Data Lake

Azure SQL Database

Job description

Описание: 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.

Задачи:

• 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

Требования:

• 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

Условия:

• 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).