LLM Agentic Trainer - UK in Aberdeen

This position is within a project with one of the foundational LLM companies. The goal is to assist these foundational LLM companies in enhancing their Large Language Models. One way we help these companies improve their models is by providing them with high-quality proprietary data. This data serves two main purposes: first, as a basis for fine-tuning their models, and second, as an evaluation set to benchmark the performance of their models or competitor models.

For example, in the case of Agent Completion (AC) data generation, your task will be to simulate high-quality multi-turn conversations between a user and a smart assistant that utilises function-calling tools to accomplish user goals. You will craft these dialogues by playing both the assistant and the user, while simulating tool use where necessary to guide the assistant through complex decision-making and real-world reasoning scenarios.

Role and Responsibilities:

• Design multi-turn conversations that simulate real user–assistant interactions using apps like calendar, email, maps, and drive.
• Write both user and assistant turns, including assistant tool calls when corrections are needed.
• Select appropriate moments for tool use to ensure logical flow and proper function-call behaviour.
• Create dialogues that demonstrate natural language, contextual understanding, and intelligent responses.
• Produce examples showing successful task completion, graceful handling of infeasible requests, and engaging general chat without unnecessary tool use.
• Follow established formatting and quality guidelines using the internal playbook.
• Iterate on conversation examples based on feedback to improve realism, clarity, and value.
• Collaborate with peers and reviewers to maintain consistency and high standards.

Requirements:

• 3+ years of professional experience in a technical or analytical field.
• Experience with any programming language or tech stack, strong understanding of APIs, data formats (e.g., JSON), and logical reasoning is more important than specific tools.
• Strong technical reasoning skills and ability to model real-world assistant behaviour using tool-based APIs.
• Ability to break down complex tasks and simulate realistic user–assistant dialogues.
• Excellent written communication skills in English, with clear and coherent instructional writing.
• Creativity and attention to detail in crafting realistic scenarios and responses.
• Experience with LLMs, virtual assistants, or function-calling frameworks is a plus.
• Ability to follow detailed guidelines and formatting standards consistently.
• Native or professional proficiency in the required locale-specific language is mandatory.