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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, collaborative culture, and meaningful challenges.
- Why this job: Shape the future of LLMs in production and make a real-world impact.
- Qualifications: Proven LLM deployment experience and strong Python skills required.
- Other info: Opportunity for career growth in a dynamic, scaling environment.
The predicted salary is between 36000 - 60000 £ 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 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 in Clive employer: Adria Solutions Ltd.
Contact Detail:
Adria Solutions Ltd. Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Llm Engineer in Clive
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with fellow LLM enthusiasts. 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 LLM projects, especially those that demonstrate your hands-on experience. This will give potential employers a taste of what you can bring to the table.
✨Tip Number 3
Prepare for interviews by brushing up on real-world applications of LLMs. Be ready to discuss your experience with prompt engineering, RAG pipelines, and how you've tackled challenges in previous roles.
✨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 Llm Engineer in Clive
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.
✨Show Off Your Python Skills
Since strong Python development skills are essential, prepare to demonstrate your coding abilities. You might be asked to solve a problem or explain your thought process while coding. Practise common algorithms and data structures that could come up during the interview.
✨Understand RAG and Embeddings
Familiarise yourself with Retrieval-Augmented Generation (RAG) and vector databases like FAISS or Pinecone. Be prepared to discuss how you've built and optimised these systems in past projects, as this will show your practical experience and understanding of the technology.
✨Be Ready for Real-World Scenarios
Since the role involves working in safety-critical environments, think about examples from your past where you've had to ensure compliance and security. Prepare to discuss how you approach problem-solving in ambiguous situations and how you translate complex AI concepts into practical solutions.
