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- Tasks: Design and deploy LLM-driven features for real-world applications.
- Company: Fast-growing AI tech company focused on safety-critical environments.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Why this job: Make a real impact by shaping the future of AI in production.
- Qualifications: Proven LLM deployment experience and strong Python skills required.
- Other info: Join a collaborative team tackling meaningful, technical challenges.
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 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!
LLM Engineer / AI Engineer – Cheshire in Northwich employer: Adria Solutions Ltd
Contact Detail:
Adria Solutions Ltd Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land LLM Engineer / AI Engineer – Cheshire in Northwich
✨Tip Number 1
Network like a pro! Reach out to folks in the AI and LLM space on LinkedIn or at local 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 any relevant work. We want to see what you can do, so make sure to highlight your hands-on experience with Python and LLMs.
✨Tip Number 3
Prepare for those interviews! Brush up on your knowledge of RAG pipelines and vector databases. We’re looking for someone who can confidently discuss trade-offs around accuracy and latency, so be ready to dive deep.
✨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 LLM Engineer / AI Engineer – Cheshire in Northwich
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 you've designed and deployed LLM-driven features, so don’t hold back on the details!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Tell us why you're passionate about AI and how your background aligns with our mission. Be specific about your hands-on experience with RAG pipelines and embeddings.
Showcase Your Problem-Solving Skills: In your application, give examples of how you've tackled complex AI challenges. We love candidates who can translate tricky concepts into practical solutions, so share your success 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’re considered for this exciting opportunity!
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, prepare to demonstrate your coding abilities. You might be asked to solve a problem on the spot, so practice writing clean, efficient code that showcases your understanding of prompt engineering and RAG pipelines.
✨Understand the Business Context
Familiarise yourself with the company's mission and the specific industry they operate in. Be prepared to discuss how your work with LLMs can impact safety-critical environments and improve decision-making processes. This shows you're not just technically skilled but also aligned with their goals.
✨Prepare for Collaboration Questions
Given the collaborative nature of the role, think about past experiences where you've worked with cross-functional teams. Be ready to share examples of how you’ve communicated complex AI concepts to non-technical stakeholders and how you’ve contributed to high-value use cases.
