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For Companies At a Glance
- Tasks: Design and deploy cutting-edge AI systems with real impact and ownership.
- Company: Join a fast-moving startup revolutionising intelligent systems with large language models.
- Benefits: Enjoy remote work options, competitive salary, and the chance to shape tech strategy.
- Why this job: Be part of a team that values quality and innovation in AI development.
- Qualifications: Experience with LLMs, Python, and building RAG pipelines is essential.
- Other info: Visa sponsorship available for the right candidates.
The predicted salary is between 43200 - 72000 £ per year.
We are a fast-moving startup building intelligent systems powered by large language models. We are looking for a software engineer who can design, build, and deploy AI systems end-to-end — especially those involving retrieval-augmented generation (RAG), embeddings, and LLM orchestration. This is a hands-on role with real impact and ownership.
What You’ll Work On
- Architect and implement RAG pipelines that combine vector search, custom retrievers, and LLM reasoning
- Own the evaluation stack — design eval harnesses, benchmarks, and regression tests for LLM outputs
- Build and scale infrastructure for deploying models and agents into real production environments
- Experiment with model behavior, latency trade-offs, and prompt tuning
- Collaborate closely with founders on product, architecture, and research priorities
Core Requirements
- Proven experience building AI systems with LLMs — you’ve worked with tools like LangChain, LlamaIndex, Haystack, or built your own stack
- Hands-on with embedding models, vector DBs (e.g., FAISS, Weaviate, Qdrant), and retrieval logic
- Strong Python engineering skills — you write clean, production-ready code with tests
- Experience building and evaluating RAG pipelines in a real-world setting
- Familiarity with LLM evaluation techniques — you don’t deploy until you’ve tested against real metrics
- Solid understanding of modern cloud infrastructure (e.g., Docker, Kubernetes, serverless, GCP/AWS)
Bonus Skills
- Built custom eval pipelines using tools like Ragas, TruLens, or your own scoring systems
- Experience tuning open-source models (e.g., Mistral, LLaMA, Falcon) or working with APIs (OpenAI, Anthropic, Cohere)
- Exposure to agentic systems, tools + memory management, or multi-step reasoning chains
- Experience in fast-paced, early-stage startup environments
Why This Role Is Unique
- You’ll be engineering AI features that ship to users, not just running experiments
- Evaluation is first-class — we’re serious about quality, not just it looks good in the demo
- You’ll help shape both tech strategy and engineering culture from day one
- We care more about what you’ve built than where you’ve worked
You should apply if:
- You’re an engineer who enjoys building production-grade AI systems, and you believe evals, not vibes, should drive development.
- You’re comfortable moving fast, debugging strange model behavior, and taking real ownership of the tech.
Note, this company is sponsoring visas.
AI RAG Engineer (City of London) employer: Nihires
Contact Detail:
Nihires Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI RAG Engineer (City of London)
✨Tip Number 1
Familiarise yourself with the specific tools mentioned in the job description, like LangChain and Haystack. Having hands-on experience with these tools will not only boost your confidence but also demonstrate your commitment to the role.
✨Tip Number 2
Engage with the AI community online, especially forums or groups focused on retrieval-augmented generation. Networking with professionals in this space can provide insights and potentially lead to referrals.
✨Tip Number 3
Prepare to discuss your past projects involving LLMs and RAG pipelines in detail. Be ready to explain your thought process, challenges faced, and how you overcame them, as this will showcase your problem-solving skills.
✨Tip Number 4
Stay updated on the latest trends in AI and cloud infrastructure. Being knowledgeable about recent advancements will help you engage in meaningful conversations during interviews and show that you're proactive about your professional development.
We think you need these skills to ace AI RAG Engineer (City of London)
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience with AI systems, particularly with large language models and retrieval-augmented generation. Use specific examples of projects you've worked on that align with the job description.
Craft a Compelling Cover Letter: In your cover letter, express your passion for building AI systems and your understanding of the company's mission. Mention any relevant tools or technologies you’ve used, such as LangChain or vector databases, to demonstrate your fit for the role.
Showcase Your Projects: If possible, include links to your GitHub or portfolio showcasing projects related to AI systems, especially those involving RAG pipelines or LLMs. This will give the hiring team a clear view of your hands-on experience.
Highlight Evaluation Techniques: Discuss your familiarity with LLM evaluation techniques in your application. Explain how you ensure quality in your work and provide examples of how you've implemented testing and evaluation in past projects.
How to prepare for a job interview at Nihires
✨Showcase Your Technical Skills
Be prepared to discuss your experience with large language models and the specific tools mentioned in the job description, like LangChain or Haystack. Bring examples of projects where you've built AI systems, focusing on your hands-on experience with RAG pipelines and embedding models.
✨Demonstrate Problem-Solving Abilities
Expect technical questions that assess your ability to troubleshoot and optimise AI systems. Prepare to explain how you've tackled challenges in previous projects, particularly around model behaviour and latency trade-offs.
✨Understand Evaluation Techniques
Since evaluation is a key focus for this role, be ready to discuss LLM evaluation techniques and how you ensure quality in your deployments. Share any experiences you have with custom evaluation pipelines or metrics you've used to assess model performance.
✨Emphasise Collaboration and Ownership
This role involves close collaboration with founders and taking ownership of tech decisions. Be prepared to talk about how you've worked in teams, contributed to product development, and taken initiative in past roles, especially in fast-paced environments.
