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A technology company in Canada is seeking a skilled machine learning engineer to develop advanced agentic systems. The role involves designing and implementing multi-agent architectures while enhancing interoperability through bespoke tooling. Ideal candidates will have over 7 years of experience in machine learning, proficiency in Python, and strong knowledge of AI frameworks like Tensorflow and PyTorch. Candidates should also possess a solid foundation in statistics and communicate effectively in English (B2+/C1 level).
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 an 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.
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).
* The salary benchmark is based on the target salaries of market leaders in their relevant sectors. It is intended to serve as a guide to help Premium Members assess open positions and to help in salary negotiations. The salary benchmark is not provided directly by the company, which could be significantly higher or lower.