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Job Description

Senior Machine Learning/AI Engineer

£100,000-£125,000 Hybrid working model: three days per week in the office (Monday, Tuesday, plus one flexible day) LDN 27 days of annual leave plus 8 bank holidays and 5 additional days, including life event, volunteering, and company-wide wellbeing days. Private medical insurance, medical cashback, life insurance, gym membership, wellbeing resources, and access to mental health support. Opportunity to work remotely from anywhere for up to 10 days per year.

We are currently partnered with a company that believes technology should empower everyone to achieve their potential.

As an Senior Machine Learning/Applied AI Engineer, the successful candidate will turn cutting-edge AI research into real-world products that make learning and development smarter, more personalised, and more impactful for thousands of users.

The Senior Machine Learning/Applied AI Engineer role sits within a growing Applied Science team and is focused on building, deploying, and scaling AI systems that operate in real production environments. This is a senior, hands-on role for engineers who have moved beyond experimentation and have delivered LLM-powered systems that directly impact products, users, or business outcomes.

Key Responsibilities

Design & Deliver AI Solutions: Partner with Product, Design, and Data teams to shape and deliver AI-powered features that drive meaningful impact for learners and create value for customers. Leverage Large Models (LLMs): Design, fine-tune, and integrate LLM-powered solutions for use cases including content , semantic search, summarisation, and personalised learning experiences. Build & Integrate Models: Develop, fine-tune, and embed machine learning models into production systems using modern AI tooling, ensuring solutions are scalable, reliable, and performant. Own the End-to-End Lifecycle: Take responsibility for the full lifecycle, from raw data and experimentation through deployment, monitoring, and continuous iteration. Measure What Matters: Track performance, accuracy, and adoption of AI features, using insights to drive continuous improvement. Enable Others: Share expertise and help make AI approachable, enabling teams across the organisation to enhance their work with AI. Lead in MLOps & Cloud Infrastructure: Build robust pipelines for model training, deployment, monitoring, and retraining using AWS and modern MLOps best practices. Champion Innovation: Stay ahead of emerging AI tools and techniques, applying them to deliver exceptional, user-focused experiences.

What the Company Is Looking For

Hands-On AI/ML Expertise: Experience building and deploying machine learning models using frameworks such as PyTorch, TensorFlow, or scikit-learn. LLM Experience: Proven experience working with large models (e.g. GPT, Claude, Gemini) in production environments, including prompt engineering, evaluation, and safety considerations. Strong Engineering Skills: Proficiency in Python and TypeScript, with experience building APIs, microservices, and cloud- applications. Modern AI Tooling: Familiarity with emerging AI development tools such as Cursor and Gemini is highly desirable. Cloud & MLOps: Practical experience deploying AI solutions on AWS, including CI/CD, model versioning, observability, and retraining pipelines. Data Fluency: Skilled in working with structured and unstructured data, including preprocessing and feature engineering. User-Centred Mindset: Ability to translate complex AI capabilities into intuitive and seamless product experiences. Collaborative Approach: Thrives in cross-functional, creative teams and values building together. Growth Mindset: Curious, open to feedback, and committed to contributing to an inclusive, high-performing culture.

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