Role overview
AI / Machine Learning Engineer – Agentic AI & LLM Systems
We’re partnered with a pioneering AI organisation pushing the boundaries of
Agentic AI
— building systems where
LLMs reason, act, and collaborate autonomously
to drive real-world outcomes.
Designing the frameworks, tools, and data loops that allow intelligent agents to think, plan, and improve themselves — transforming raw model power into adaptive, high-performing AI systems.
What you'll work on
- Build Agentic Systems – Develop and optimise LLM-based agents that can reason, plan, and execute multi-step tasks autonomously.
- Enhance LLM Reasoning – Apply reinforcement learning, tool use, and reflection techniques to strengthen decision-making and contextual understanding.
- Design Scalable Frameworks – Create data pipelines, annotation tools, and evaluation flywheels that accelerate model iteration and feedback loops.
- Collaborate with Research Teams – Translate experimental findings into production-grade systems that extend the autonomy and reliability of agents.
- Run Experiments End-to-End – Own your compute environment (e.g. Jupyter, Colab, Databricks) and iterate on large-scale LLM training and evaluation.
What we're looking for
- 4+ years’ experience in Machine Learning or AI , with exposure to LLM agent systems , tool-use frameworks, or generative AI.
- Advanced proficiency in Python and ML frameworks such as PyTorch or TensorFlow .
- Hands-on experience developing or fine-tuning LLaMA , GPT , or similar foundation models.
- Understanding of agentic architectures , chain-of-thought reasoning, and memory/reflection mechanisms.
- Proven ability to debug, optimise, and scale ML experiments and compute pipelines.
- MSc or PhD in AI, Computer Science, or related field .
Tags & focus areas
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