Senior AI/ML Engineer Lead - APPLIED SCIENCE
Engineer and optimize cutting-edge LLMs to deliver impactful AI-driven products.
Full-time Permanent Role
Downtown, Toronto - Hybrid
Start Date - by March 2026
Enterprise client, global team with centre of excellence here in their Toronto Offices.
Core Responsibilities
• Collaborate with AI engineers and technical teams to architect and deploy LLM-based solutions addressing complex business challenges in cloud environments (including CPU and GPU configurations).
• Research and apply cutting-edge techniques for LLM development, such as pre-training, fine-tuning, alignment strategies, and prompt engineering, while exploring broader generative AI capabilities.
• Develop and curate novel datasets to enable LLMs to perform new tasks, and build scalable, repeatable pipelines for data collection and processing using Python and modern software engineering practices.
• Write and maintain high-quality, production-ready code aligned with organizational standards and objectives.
• Create reusable tools, frameworks, and workflows to streamline generative AI and LLM operations.
• Communicate project progress and manage stakeholder expectations effectively.
• Adapt to changing priorities while maintaining delivery timelines and project momentum.
Required Qualifications
• 5+ years of experience in AI engineering or machine learning, with a strong focus on LLMs and proficiency in Python for production-level coding.
• Deep understanding of the LLM lifecycle, including dataset creation for pre-training, instruction tuning, and preference alignment, as well as deployment strategies.
• Strong problem-solving skills and ability to communicate technical concepts clearly to both technical and non-technical stakeholders.
• Hands-on experience with LLM frameworks and libraries (e.g., Transformers, TRL, DeepSpeed, PyTorch) and practical implementation of ML techniques at scale.
• Expertise in distributed systems and high-performance architectures.
• Solid foundation in NLP, including text representation, semantic extraction, and related modeling techniques.
• Familiarity with containerization technologies such as Kubernetes and Docker.
