APTPUO Fall 2026- MIA5150-Topic (Generative AI and (LLMs)

University of Ottawa•Ottawa, ON

13d•Remote

About The Position

Foundations and practices of Generative AI and Large Language Models (LLMs), covering the full lifecycle from model development to deployment. Explore generative model families, including Transformers, autoregressive models, diffusion models, GANs, and VAEs, and their multimodal applications across text, image, audio, and video. Modern techniques such as fine-tuning, parameter-efficient training, in-context learning, contrastive learning, retrieval-augmented generation (RAG), and multi-agent systems for enabling complex reasoning, coordination, and autonomous task execution are discussed. Key practices in prompt engineering, inference optimization, safety and alignment, and responsible AI deployment. Practical applications across diverse domains.

Requirements

Ph.D. in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Software Engineering, DTI, Engineering, or a closely related field.
Demonstrated expertise in Generative AI and Large Language Models (LLMs), including areas such as Transformers, diffusion models, GANs, VAEs, retrieval-augmented generation (RAG), prompt engineering, fine-tuning, and multimodal AI systems.
Experience developing or applying modern AI/ML workflows using industry-standard tools and frameworks such as Python, PyTorch, TensorFlow, Hugging Face Transformers, LangChain, vector databases, and cloud-based AI platforms.
Knowledge of AI deployment practices, including inference optimization, parameter-efficient training, model evaluation, safety, alignment, responsible AI, and scalable deployment architectures.
Knowledge of emerging agentic AI systems and multi-agent orchestration frameworks for autonomous reasoning, planning, and task execution.
Teaching experience at the graduate/undergraduate level in Artificial Intelligence, Machine Learning, Data Science, or related disciplines.
Demonstrated ability to translate complex AI concepts into applied, industry-relevant learning experiences through lectures, labs, projects, and case studies.

Nice To Haves

Relevant industry experience in AI/ML development, applied Generative AI, MLOps, or AI product development is considered an asset.

Responsibilities

Teach Generative AI and Large Language Models (LLMs) covering the full lifecycle from model development to deployment.
Explore generative model families, including Transformers, autoregressive models, diffusion models, GANs, and VAEs, and their multimodal applications across text, image, audio, and video.
Discuss modern techniques such as fine-tuning, parameter-efficient training, in-context learning, contrastive learning, retrieval-augmented generation (RAG), and multi-agent systems for enabling complex reasoning, coordination, and autonomous task execution.
Cover key practices in prompt engineering, inference optimization, safety and alignment, and responsible AI deployment.
Discuss practical applications across diverse domains.
Translate complex AI concepts into applied, industry-relevant learning experiences through lectures, labs, projects, and case studies.