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- Tasks: Deploy and integrate AI models into real-world products, ensuring reliability and performance.
- Company: Fast-scaling AI tech company with a focus on innovation and collaboration.
- Benefits: Competitive salary up to £125,000, bonuses, stock options, and hybrid work model.
- Why this job: Join a new team and make a tangible impact in the AI landscape.
- Qualifications: Experience with deploying machine learning systems and strong Python skills required.
- Other info: Exciting opportunity for career growth in a dynamic environment.
The predicted salary is between 100000 - 150000 £ per year.
We’re working with a fast-scaling, venture-backed AI and technology business that partners with leading organisations to embed AI capability into the workforce at scale. Their products are already used by thousands of learners and enterprises, and they’re now doubling down on operationalising AI in production.
The Applied AI Engineer will join a brand new team, sitting at the intersection of product engineering, AI and platform engineering. You’ll work closely with Product, Design and Data teams to turn AI capabilities into dependable, user-facing features.
Key responsibilities:- Deploying and integrating AI models (including LLMs) into production systems and user-facing products.
- Designing and implementing LLM-powered workflows for use cases such as content generation, semantic search, summarisation, and personalisation.
- Building APIs, services and pipelines that enable AI features to run at scale, securely and reliably.
- Owning the end-to-end delivery of AI features: from experimentation and integration through to launch, monitoring, and iteration.
- Establishing strong MLOps practices, including deployment pipelines, monitoring, evaluation, rollback strategies, and retraining workflows.
- Measuring feature performance, latency, accuracy, cost, and adoption — and improving based on real usage.
- Acting as a bridge between technical and non-technical teams, helping others understand what AI can (and can’t) do in production.
- Strong experience deploying machine learning or LLM-based systems into production.
- Hands-on experience working with LLMs (e.g. GPT, Claude, Gemini), including prompt engineering, orchestration, evaluation and safety considerations.
- Excellent software engineering skills in Python with experience building APIs and backend services.
- Practical experience running AI systems on AWS, including CI/CD, model versioning, monitoring, and observability.
- Familiarity with MLOps concepts such as deployment pipelines, model monitoring and retraining (rather than model research).
- Experience working with structured and unstructured data in production environments.
- A product-focused mindset — you care about usability, performance, reliability, and real-world impact.
- Comfortable collaborating closely with Product, Design and Data teams.
- Experience with modern AI tooling platforms (e.g. Cursor, Gemini) is a strong advantage.
This is a fantastic opportunity for an Applied AI Engineer to join a new team in a high-growth company. Please reply with your CV or call Simon for a chat.
Applied AI Engineer in London employer: Burns Sheehan
Contact Detail:
Burns Sheehan Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Applied AI Engineer in London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with potential colleagues on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving AI models and LLMs. This will give you an edge and demonstrate your hands-on experience to potential employers.
✨Tip Number 3
Prepare for interviews by brushing up on common technical questions related to deploying AI systems. Practice explaining complex concepts in simple terms, as you'll need to bridge the gap between technical and non-technical teams.
✨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 Applied AI Engineer in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with deploying AI models and working with LLMs. We want to see how your skills align with the role, so don’t be shy about showcasing relevant projects!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re excited about the Applied AI Engineer role and how your background makes you a perfect fit for our team. Keep it engaging and personal.
Showcase Your Technical Skills: We’re looking for strong software engineering skills, especially in Python. Make sure to mention any experience you have with APIs, AWS, and MLOps practices. The more specific, the better!
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity. We can’t wait to hear from you!
How to prepare for a job interview at Burns Sheehan
✨Know Your AI Models Inside Out
Make sure you’re well-versed in the AI models and LLMs mentioned in the job description, like GPT or Claude. Be ready to discuss your hands-on experience with these models, including prompt engineering and safety considerations.
✨Showcase Your Software Skills
Brush up on your Python skills and be prepared to talk about your experience building APIs and backend services. You might even want to bring examples of your work to demonstrate your software engineering prowess.
✨Understand MLOps Practices
Familiarise yourself with MLOps concepts such as deployment pipelines and model monitoring. Be ready to discuss how you’ve implemented these practices in past roles, as this will show you can bridge the gap between technical and non-technical teams.
✨Emphasise Collaboration
This role requires working closely with Product, Design, and Data teams. Prepare examples of how you’ve successfully collaborated in the past, highlighting your product-focused mindset and how you ensure usability and performance in your projects.
