At a Glance
- Tasks: Design and deploy innovative AI/ML solutions in a collaborative environment.
- Company: Join Sage, a leading global academic publisher committed to research and education.
- Benefits: Enjoy 25 days leave, hybrid working, private medical cover, and professional development support.
- Other info: Be part of a diverse team that values authenticity and belonging.
- Why this job: Make a real impact in the world of AI/ML while shaping the future of education.
- Qualifications: Experience in MLOps, NLP, and programming languages like Python or R.
The predicted salary is between 63000 - 77000 £ per year.
About Sage: Sage is 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
About The Role: 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.
Your New Role:
- 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
Can you be our new Machine Learning Engineer?
- 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
Overview of benefits:
- 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)
We’re a Disability Confident Employer (Level 1).
Diversity, Equity, and Inclusion: At Sage, we are committed to building a diverse and inclusive team and to sustaining a culture that celebrates diversity, encourages authenticity, and builds a deep sense of belonging. We encourage applications from all members of society irrespective of age, disability, sex or gender identity, sexual orientation, colour, race, nationality, ethnic or national origin, religion, or belief as crafting value through diversity is what makes us strong.
We’re committed to making our recruitment process accessible and inclusive. If you’d like to request reasonable adjustments, please email.
Machine Learning Engineer employer: Sage
Sage is an exceptional employer located in Newcastle, offering a dynamic work culture that fosters collaboration and innovation. As a Digital Content Designer, you'll benefit from a hybrid working model, allowing for flexibility while engaging with talented teams across Product, UX, and Engineering. With a strong focus on employee growth and development, Sage provides ample opportunities to enhance your skills and make a meaningful impact on customer experiences through cutting-edge technology.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer
✨Get Involved in Data Science Meetups
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We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Sage, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Sage. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Sage
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Sage!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
