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- Tasks: Design and deploy cutting-edge AI solutions for a global fitness brand.
- Company: Join a leading fitness and wellness brand with a strong focus on AI innovation.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Why this job: Shape the future of AI in a dynamic environment with real impact.
- Qualifications: 4-6 years of experience in AI engineering and strong Python skills.
- Other info: Be part of a new AI team with top-level support and exciting projects.
The predicted salary is between 36000 - 60000 £ per year.
Do you want to build production GenAI products (not prototypes) in a business with real scale and board-level visibility? Have you shipped LLM/RAG systems end-to-end and owned them in production? Are you ready to join a new AI team early and shape how AI is delivered across a global consumer brand?
A global fitness and wellness brand is building a new Group AI function to deliver high-impact AI solutions across the organisation. With new leadership in place (including a newly appointed Chief AI Officer), this team has top-down backing and a clear mandate to ship measurable AI outcomes across both customer-facing and internal operational use cases. For example, app-based agents and optimization algorithms for physical spaces (maintenance, costs, customer experience).
As an AI Engineer, you’ll build and deploy end-to-end AI solutions across the company, working closely with stakeholders and owning delivery from brief to production. This is a builder role for someone who enjoys taking ambiguous problems, designing a practical AI solution, and deploying it into production. You’ll work across both GenAI and applied ML, building scalable AI services that directly improve customer experience and business performance.
Key responsibilities:- Design, build and deploy end-to-end AI solutions
- Develop LLM solutions (e.g. RAG, workflow orchestration, evaluation)
- Build APIs/services integrating data sources and AI outputs
- Translate stakeholder needs into practical technical delivery plans
- Own delivery quality: reliability, performance, observability, iteration
- Collaborate across engineering, data, operations, and product teams
- 4-6+ years of industry experience
- Strong Python and SQL
- Hands-on GenAI delivery (RAG/LLMs; LangChain-style tooling strongly preferred)
- Comfortable operating independently with real ownership
- Solid stakeholder management and ability to explain clearly to non-technical audiences
- DS background that has moved into AI engineering / LLM product delivery is ideal
- Bonus: Databricks, Azure, MLOps/CI-CD experience
Interested? Please apply below.
Artificial Intelligence Engineer - Fitness & Health in London employer: Harnham
Contact Detail:
Harnham Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Artificial Intelligence Engineer - Fitness & Health in London
✨Tip Number 1
Network like a pro! Reach out to people in the fitness and health industry, especially those working with AI. Use platforms like LinkedIn to connect and engage with them. You never know who might have the inside scoop on job openings!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your previous AI projects, especially any LLM or RAG systems you've built. This will give potential employers a clear view of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on your problem-solving skills. Be ready to discuss how you’ve tackled ambiguous problems in the past and how you can apply that to building AI solutions in a real-world setting.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Artificial Intelligence Engineer - Fitness & Health in London
Some tips for your application 🫡
Show Your Passion for AI: When writing your application, let your enthusiasm for AI and its impact on fitness and health shine through. We want to see how your experience aligns with our mission to deliver high-impact AI solutions.
Be Specific About Your Experience: Detail your hands-on experience with LLMs and RAG systems. We’re looking for concrete examples of projects you've shipped and how you’ve owned them in production. This helps us understand your journey and expertise.
Tailor Your Application: Make sure to customise your application to reflect the job description. Highlight your skills in Python, SQL, and any relevant tools like LangChain. We appreciate when candidates take the time to connect their background to what we’re looking for.
Apply Through Our Website: Don’t forget to submit your application through our website! It’s the best way for us to receive your details and ensures you’re considered for this exciting opportunity in our new AI team.
How to prepare for a job interview at Harnham
✨Know Your AI Stuff
Make sure you brush up on your knowledge of GenAI and LLMs. Be ready to discuss your hands-on experience with RAG systems and how you've delivered them end-to-end. Prepare examples that showcase your ability to translate complex technical concepts into practical solutions.
✨Showcase Your Problem-Solving Skills
This role is all about taking ambiguous problems and turning them into actionable AI solutions. Think of specific challenges you've faced in previous roles and how you approached them. Highlight your design process and the impact your solutions had on customer experience or business performance.
✨Communicate Like a Pro
You’ll need to explain technical details to non-technical stakeholders, so practice simplifying your language. Prepare to discuss how you’ve managed stakeholder expectations in the past and how you ensure everyone is on the same page throughout the project lifecycle.
✨Be Ready to Collaborate
Collaboration is key in this role, so think about times when you worked closely with engineering, data, and product teams. Be prepared to share how you’ve contributed to team success and how you handle differing opinions or challenges in a collaborative environment.
