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- Tasks: Lead the development of AI systems and collaborate with a top-notch team.
- Company: Join an innovative data-driven organisation at the forefront of AI technology.
- Benefits: Enjoy remote work, equity options, 25 days holiday, and a matched pension.
- Why this job: Be part of a dynamic environment that values creativity and continuous learning.
- Qualifications: Experience in data science or machine learning, strong Python skills, and familiarity with ML frameworks required.
- Other info: Work abroad for up to 3 months and engage in exciting projects.
The predicted salary is between 43200 - 72000 £ per year.
Applied AI Engineer – AI Product Deployment | High-Growth Tech Company
London – Hybrid – 3 days a week in the office
Up to £125,000 per annum plus bonus and stock
Applied AI Engineer – 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 role
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 include:
- 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 strongMLOps 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.
What they’re looking for
- 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.
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Artificial Intelligence Engineer employer: Burns Sheehan
Contact Detail:
Burns Sheehan Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Artificial Intelligence Engineer
✨Tip Number 1
Familiarise yourself with the latest AI trends and technologies relevant to the role. This will not only help you in interviews but also demonstrate your genuine interest in the field and the company.
✨Tip Number 2
Network with current employees or alumni who work in similar roles. They can provide insights into the company culture and expectations, which can be invaluable during your application process.
✨Tip Number 3
Prepare to discuss specific projects where you've applied machine learning techniques. Be ready to explain your thought process, challenges faced, and how you overcame them, as this showcases your problem-solving skills.
✨Tip Number 4
Stay updated on the company's recent projects and achievements in AI. Mentioning these during your conversations can show that you're proactive and genuinely interested in contributing to their success.
We think you need these skills to ace Artificial Intelligence Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in data science, engineering, or machine learning. Emphasise your skills in Python, SQL, and any hands-on experience with NLP or LLMs, as these are crucial for the role.
Craft a Compelling Cover Letter: In your cover letter, express your passion for AI and innovation. Mention specific projects where you've applied machine learning techniques and how you can contribute to the company's goals.
Showcase Your Projects: If you have worked on relevant projects, consider including a portfolio or links to your work. Highlight your experience across the full model lifecycle and any familiarity with ML frameworks like PyTorch.
Prepare for Technical Questions: Be ready to discuss your technical skills and experiences in detail. Prepare examples of how you've driven model development and collaborated with teams, as these will likely come up during interviews.
How to prepare for a job interview at Burns Sheehan
✨Showcase Your Technical Skills
Be prepared to discuss your experience with Python, SQL, and machine learning frameworks like PyTorch and scikit-learn. Bring examples of projects where you've successfully deployed AI models, as this will demonstrate your hands-on expertise.
✨Understand the Company’s AI Focus
Research the company’s current AI projects and innovations. Familiarise yourself with their use of NLP, LLMs, and recommendation systems. This knowledge will help you tailor your responses and show genuine interest in their work.
✨Prepare for Problem-Solving Questions
Expect to face questions that assess your ability to translate complex problems into actionable projects. Practice articulating your thought process and how you approach challenges, especially in a dynamic environment.
✨Emphasise Collaboration and Mentorship
Highlight your experience working in cross-functional teams and mentoring others. The role involves collaboration, so sharing examples of how you've contributed to team success will resonate well with the interviewers.
