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- Tasks: Design and deploy LLM-driven features in live products and platforms.
- Company: Fast-growing AI tech company focused on real-world applications.
- Benefits: High-impact role with ownership and collaborative culture.
- Why this job: Shape the future of LLMs in production and tackle meaningful challenges.
- Qualifications: Proven LLM deployment experience and strong Python skills required.
- Other info: Opportunity to work in a dynamic, scaling environment.
The predicted salary is between 48000 - 72000 £ per year.
My client is a fast-growing AI technology company building intelligent systems deployed in real-world, safety-critical environments. Their solutions combine advanced AI, data, and edge technologies to support decision-making and reduce risk in high-hazard industrial settings.
They are now looking to hire a Senior LLM Engineer to help design and deliver large language model powered capabilities across internal platforms and customer-facing products. This is a hands-on, production-focused role for someone with strong real-world LLM experience, not a purely research or experimental background.
The Role
- Design, build and deploy LLM-driven features into live products and platforms
- Work with both commercial and open-source LLMs, selecting the right model for each use case
- Build and optimise RAG pipelines, embeddings and vector-based retrieval solutions
- Develop APIs and services that integrate LLMs with existing AI, data and platform systems
- Optimise solutions for performance, reliability, latency and cost
- Collaborate with engineering, AI and product teams to identify and deliver high-value use cases
- Ensure all solutions meet security, compliance and data governance standards
What My Client Is Looking For
Essential
- Proven experience deploying LLMs in production
- Strong Python development skills
- Hands-on experience with:
- Prompt engineering and evaluation
- Retrieval-Augmented Generation (RAG)
- Embeddings and vector databases (e.g. FAISS, Pinecone, Weaviate, Chroma)
Highly Desirable
- Experience working with or fine-tuning open-source LLMs
- Familiarity with LLMOps (monitoring, evaluation, guardrails, versioning)
- Experience integrating LLMs into complex or data-heavy systems
- Docker and Linux experience
- Background in regulated, industrial or safety-critical environments
The Right Mindset
- Pragmatic, delivery-focused and comfortable working with ambiguity
- Able to translate complex AI concepts into practical solutions
- Confident owning problems end-to-end, from idea through to deployment
- Motivated by building AI that has real-world impact
Why Apply?
- High-impact role with real ownership
- Opportunity to shape how LLMs are used in production environments
- Work on meaningful, technically challenging problems
- Collaborative engineering culture within a scaling business
Interested? Please Click Apply Now!
Senior LLM Engineer / AI Engineer – Cheshire in London employer: Adria Solutions Ltd
Contact Detail:
Adria Solutions Ltd Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior LLM Engineer / AI Engineer – Cheshire in London
✨Tip Number 1
Network like a pro! Reach out to folks in the AI and LLM space on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can get your foot in the door.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your LLM projects or contributions. We want to see your hands-on experience, so make sure to highlight any real-world applications you've worked on.
✨Tip Number 3
Prepare for those interviews! Brush up on your Python skills and be ready to discuss your experience with RAG pipelines and embeddings. We’re talking about practical solutions, so be ready to dive deep into your past projects.
✨Tip Number 4
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 take the initiative to connect directly with us.
We think you need these skills to ace Senior LLM Engineer / AI Engineer – Cheshire in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your real-world LLM experience and Python skills. We want to see how your background aligns with the role, so don’t be shy about showcasing relevant projects or achievements!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about AI and how you can contribute to our mission. Keep it concise but impactful – we love a good story!
Showcase Your Technical Skills: When detailing your experience, focus on specific technologies like RAG pipelines and vector databases. We’re looking for hands-on experience, so include examples of how you’ve used these in production environments.
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’re considered for this exciting opportunity. Don’t miss out!
How to prepare for a job interview at Adria Solutions Ltd
✨Know Your LLMs Inside Out
Make sure you brush up on your knowledge of large language models, especially the ones mentioned in the job description. Be ready to discuss your hands-on experience with deploying LLMs in production and how you've tackled challenges like accuracy and latency.
✨Showcase Your Python Skills
Since strong Python development skills are essential for this role, prepare to demonstrate your coding abilities. You might be asked to solve a problem or explain your approach to building LLM-backed services using frameworks like FastAPI or Flask.
✨Understand RAG and Vector Databases
Familiarise yourself with Retrieval-Augmented Generation (RAG) and vector databases like FAISS or Pinecone. Be prepared to discuss how you've built and optimised RAG pipelines and how they can enhance LLM performance in real-world applications.
✨Emphasise Collaboration and Problem-Solving
This role requires working closely with various teams, so highlight your collaborative experiences. Share examples of how you've owned problems from idea to deployment, and how you’ve translated complex AI concepts into practical solutions that have made an impact.
