This role is for a Research Engineer specialising in AI systems powered by Large Language Models. The goal is to design and build multi-agent AI systems, essentially, AI "teams" that can collaborate, debate, and solve complex problems together.
The engineer will:
- Develop and test AI agents that can handle reasoning, teamwork, and creativity.
- Use frameworks like LangChain to develop, test, and deploy AI agents.
- Improve how agents use knowledge (through tools like RAG - Retrieval Augmented Generation) so they stay accurate and avoid "hallucinations"
- Keep up with the latest AI research and bring new ideas into the company.
- Work with data scientists and engineers to integrate these systems into real products.
- Measure how well the AI agents work and continuously refine them.
Overall, the role combines research, software development, and applied machine learning, with a strong emphasis on LLMs and agent-based systems.
Education & Background for the Research Engineer LLM :
Master's or PhD in Computer Science, Artificial Intelligence, Machine Learning, or NLP.
Strong academic foundation in deep learning, computational linguistics, or applied ML.
Technical ExperienceLLM & Multi-Agent Systems :
- Direct, hands-on experience in building agents or multi-agent AI systems using Large Language Models (LLMs).
- Knowledge of agent orchestration frameworks like LangChain or LlamaIndex.
- Familiarity with agent collaboration methods (debate, cooperative reasoning, creative problem-solving)
Programming & Frameworks :
- Proficiency in Python
- Experience with ML/DL libraries such as PyTorch or TensorFlow.
- Skilled in debugging, model prototyping, and performance optimisation
Deep Learning & NLP Knowledge :
- Strong grasp of Transformers, GPT models, and BERT-like architectures.
- Understanding of model fine-tuning, transfer learning, and prompt engineering.
- Familiarity with NLP tasks: information retrieval, summarisation, question-answering.
Data & Knowledge Systems :
- Experience with vector databases (e.g., Pinecone, Weaviate, FAISS).
- Knowledge graphs for reasoning and structured data use.
- Retrieval-Augmented Generation (RAG) techniques to ground LLMs in factual data.
Research & Innovation :
- Academic publications, conference presentations
- Ability to evaluate, benchmark, and iteratively optimise AI systems.
Relocation and Visa Sponsorship supported by the client.
Permanent full time contract