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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.
- Other info: Exciting opportunity for career growth in a dynamic environment.
The predicted salary is between 90000 - 140000 £ 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. They’re hiring an Applied AI Engineer to focus on deploying, integrating, and scaling AI and LLM-powered systems inside real products. This is not a research or pure model-development role — it’s for engineers who enjoy taking models that already exist and making them reliable, observable, and valuable in the real world.
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 employer: Burns Sheehan
Contact Detail:
Burns Sheehan Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Applied AI Engineer
✨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 gives you a chance to demonstrate your hands-on experience and problem-solving abilities in real-world scenarios.
✨Tip Number 3
Prepare for interviews by brushing up on common technical questions related to deploying AI systems. Be ready to discuss your experience with MLOps practices and how you've tackled challenges in past projects.
✨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, it shows you’re genuinely interested in joining our team at StudySmarter.
We think you need these skills to ace Applied AI Engineer
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 Problem-Solving Skills: In your application, give examples of how you've tackled challenges in deploying AI systems. We love seeing how you’ve made models reliable and valuable in real-world scenarios, so share those stories!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important updates from our team!
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
Prepare to demonstrate your Python programming skills, especially in building APIs and backend services. Have examples ready that highlight your experience in deploying machine learning systems into production.
✨Understand MLOps Practices
Brush up on MLOps concepts such as deployment pipelines and model monitoring. Be prepared to discuss how you’ve implemented these practices in past roles, as this will show your readiness for the responsibilities of the position.
✨Emphasise Collaboration
Since the role involves working closely with Product, Design, and Data teams, be ready to share examples of how you’ve successfully collaborated with cross-functional teams in the past. Highlight your ability to bridge technical and non-technical discussions.
