Job description

Description:
We are seeking professionals with a background in developing advanced agentic systems. The role involves designing, implementing, and optimising multi-agent architectures using frameworks such as Autogen and CrewAI. This includes developing bespoke tooling and custom function calls to enhance agent interoperability while applying Retrieval-Augmented Generation (RAG) methodologies for task execution and problem-solving. Additionally, the role requires expertise in fine-tuning large language models, particularly open-source models such as Llama. Understanding of techniques such as Supervised Fine Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF) is a plus. The ideal candidate will have demonstrable, hands-on experience in agentic systems development and a understanding of the underlying technical concepts, including model optimisation and custom function integration. A solid foundation in Natural Language Processing is important, complemented by strong analytical skills necessary for troubleshooting complex issues in multi-agent and deep learning environments. The candidate must be capable of working both independently and collaboratively in a fast-paced setting, and should be driven by a passion for advancing AI technology through innovative and efficient solutions.

Requirements:
7+ years of experience in machine learning engineering, with a strong foundation in statistics, mathematics, or a related field. 3+ years of demonstrated experience in designing and implementing machine learning models and data pipelines in production environments. Proficient in Python and strong understanding of AI and machine learning algorithms and frameworks (e.g. Tensorflow, PyTorch) Strong knowledge of data management systems, both SQL (e.g., PostgreSQL) and NoSQL (e.g.MongoDB).

Soft skills: