Machine Learning Engineer (AI)

TL;DR

Machine Learning Engineer (AI): Building and optimizing ML and generative AI solutions for a data platform with an accent on defining best practices, tooling, and the ML engineering function, ensuring models transition to scalable, production-ready solutions. Focus on automating the end-to-end data science lifecycle, leveraging CI/CD and infrastructure as code, and writing high-quality Python code for model development and deployment.

Location: Hybrid in York or Manchester, UK

Company

hirify.global UK is a leading insurance brand, recognised for setting standards, delivering strong growth, and providing specialist insurance tailored to diverse and unique customer needs.

What you will do

• Contribute to the design and evolution of the Data Science platform, helping define best practices and tooling.
• Automate the end-to-end data science lifecycle, leveraging CI/CD and infrastructure as code.
• Collaborate on all aspects of data science and deployment, covering traditional ML and generative solutions.
• Work collaboratively with data engineers, software engineers, and business stakeholders.
• Write high-quality Python code following industry best practices for model development, deployment, and maintainability.
• Contribute technically to data science modeling and project workflows, including architecture discussions and deployment strategies.

Requirements

• Proven track record in data science or ML engineering roles within a business setting.
• Strong Python programming skills and wider software engineering best practices.
• Good understanding of core data science principles.
• Experience with production-level cloud-native deployment of machine learning services, using containerisation and Kubernetes, ideally with Azure and Databricks.
• Utilisation of an industry-standard software stack for data and software, including VCS (git), CI/CD (Azure DevOps desirable), and Project Management (JIRA).
• Experience deploying data science models to solve real-world business problems in production, ideally within a regulated industry such as finance or insurance.

Nice to have

• Experience utilising LLMs, generative or agentic AI in a commercial setting.

Culture & Benefits

• Comprehensive benefits package designed to support financial, physical, and personal wellbeing.
• Flexible hybrid working model, set by the team to manage work-life balance.
• Opportunities for professional development, including financial support for qualifications and training.
• Commitment to diversity and creating a truly inclusive culture.
• Environment to grow, thrive, and be rewarded for your contributions.

Hiring process

• Initial Screening Call with Talent Acquisition.
• Informal Call with the Hiring Manager.
• Technical Take-home Task (approx. 2–3 hours).
• Technical Interview.
• Business Stakeholder Interview.