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For Companies At a Glance
- Tasks: Build and deploy real-world LLM systems that make a difference.
- Company: Join a small, innovative team focused on impactful AI solutions.
- Benefits: Earn up to €110,000 with competitive salary and visa sponsorship.
- Why this job: Take ownership of cutting-edge projects and collaborate with top experts.
- Qualifications: Strong Python skills and experience with LLM systems in production.
- Other info: Work in a dynamic environment with opportunities for growth and real impact.
The predicted salary is between 66000 - 88000 £ per year.
Our client is a small, highly technical team building real-world LLM-powered systems and agentic applications - tools that are already in the hands of users, not just demos or experiments. They focus on solving meaningful problems with production-grade AI, combining strong engineering fundamentals with thoughtful experimentation. The team is pragmatic, product-oriented, and deeply cares about clean code, fast iteration, and systems that scale.
They are now looking for an LLM Engineer to take ownership of language-model systems end to end - from training and fine-tuning through to evaluation and deployment.
What You’ll Get- Competitive salary - up to €110,000 salary, depending on experience
- Ownership of production LLM systems at scale, with the freedom to work across the full ML lifecycle on top of strong, production-ready infrastructure
- Close collaboration with senior researchers and experienced engineers
- Own the full ML lifecycle — training, fine-tuning, evaluation, and deployment of LLM systems into production
- Design, build, and scale LLM-powered and agentic applications in Python, including RAG pipelines, orchestration logic, and multi-model workflows using modern LLM APIs (OpenAI, Anthropic, Gemini)
- Deploy and operate models on production-grade infrastructure, including Kubernetes, cloud platforms, and multi-GPU environments
- Strong Python engineering fundamentals with hands-on experience building and deploying LLM-based systems in production
- Solid understanding of RAG architectures, embeddings, evaluation techniques, and modern LLM tooling (e.g. PyTorch, Hugging Face, FastAPI)
- Experience working with production ML infrastructure, including model deployment, monitoring, and iteration
- Experience with agentic workflows, tool calling, or multi-step LLM systems
- Familiarity with ML observability and experimentation tools (MLflow, W&B)
- Experience working with computer vision or multi-modal AI systems
- Comfort deploying models on cloud infrastructure (AWS/GCP, Kubernetes)
Ready to build LLM systems that power real products used at scale - not hype-driven experiments? Apply now to work on pragmatic, production-first AI with real users and real impact.
LLM Engineer | 110K | London (visa sponsorship available) employer: Bluebird
Contact Detail:
Bluebird Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land LLM Engineer | 110K | London (visa sponsorship available)
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with potential colleagues on LinkedIn. 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, experiments, or any relevant work. This gives you a chance to demonstrate your hands-on experience and passion for building real-world applications.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python and ML fundamentals. Practice coding challenges and be ready to discuss your past projects in detail. We want to see how you think and solve problems!
✨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 | 110K | London (visa sponsorship available)
Some tips for your application 🫡
Show Your Passion for LLMs: When writing your application, let us see your enthusiasm for LLMs and AI! Share specific projects or experiences that highlight your skills in building and deploying language-model systems. We love seeing candidates who are genuinely excited about the technology.
Tailor Your CV and Cover Letter: Make sure to customise your CV and cover letter to match the job description. Highlight your Python engineering skills and any hands-on experience with RAG architectures or modern LLM tooling. We want to see how your background aligns with what we're looking for!
Be Clear and Concise: Keep your application clear and to the point. Use bullet points where possible to make it easy for us to read through your qualifications and experiences. We appreciate a well-structured application that gets straight to the good stuff!
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows us you’re serious about joining our team at StudySmarter!
How to prepare for a job interview at Bluebird
✨Know Your LLMs Inside Out
Make sure you brush up on your knowledge of language models, especially the ones mentioned in the job description like OpenAI and Hugging Face. Be ready to discuss your hands-on experience with training, fine-tuning, and deploying these models, as well as any challenges you've faced and how you overcame them.
✨Showcase Your Python Skills
Since strong Python engineering fundamentals are crucial for this role, prepare to demonstrate your coding skills. You might be asked to solve a problem or even write some code during the interview, so practice common algorithms and data structures relevant to LLM systems.
✨Familiarise Yourself with RAG Architectures
Understanding Retrieval-Augmented Generation (RAG) architectures is key for this position. Be prepared to explain how you would implement RAG pipelines and discuss your experience with embeddings and evaluation techniques. This will show that you can think critically about the architecture of LLM systems.
✨Discuss Real-World Applications
The team is focused on building real-world applications, so come prepared with examples of projects you've worked on that had a tangible impact. Talk about how you approached the full ML lifecycle, from deployment to monitoring, and how your work contributed to solving meaningful problems.
