Research Engineer LLM

• Growing Research center with direct industrial application
• LLM, RAG, Longchain

Company

Our client 's a global ICT company recognised for their innovations in the electronic worlds. They have a solid research centers network spread in Europe. In Amsterdam their team is specialised on computing technology such as OS, Cloud, AI and LLM.

The role of Research Engineer LLM must be based on-site in Amsterdam

Role

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.

Candidate

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)

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.

Deal

Relocation and Visa Sponsorship supported by the client.

Permanent full time contract