- Design and plan CBC/Radio-Canada’s future technology infrastructure - Optimize Models, Inference and GPU Infrastructure - Design and build high-performance training and inference systems for LLMs and multimodal AI models - Collaborate with the Technology & Infrastructure (T&I;) team to design, right-size and evolve our internal GPU cluster - Plan and develop the integration of AI solutions within CBC/Radio-Canada’s media production environments - Mentor and elevate the organization’s engineers and data scientists in large-scale ML system design and performance engineering Requirements - Bachelor's or master's degree in software engineering, information technology, artificial intelligence, mathematics or a related natural science field - Functional bilingualism (English and French) essential for Canada-wide communications - At least five years’ proven experience developing and deploying AI/ML solutions - At least eight years’ experience building tools and platforms in a software engineering role - Demonstrated experience working with language models and designing solutions optimized for cost efficiency and scale - Strong conceptual understanding of LLM, RAG and AI agent architectures, including their frameworks and operational constraints - Experience selecting AI framework architectures (e.g., TensorFlow, PyTorch, Hugging Face), cloud platforms (Azure, AWS, GCP) and orchestration tools (Docker, Kubernetes) for scalable enterprise AI solutions - Knowledge of ModelOps, AI engineering, DevOps and MLOps practices (including CI/CD pipelines)



- Solid understanding of machine learning and deep learning fundamentals - Solid technical documentation skills, with the ability to produce diagrams, demos and technical artifacts that make AI architectures understandable and actionable - Hands-on technical experience working with media production platforms (MAM/PAM) and designing scalable solutions in a highly available, 24/7 environment - Solid working knowledge of cloud technologies (AWS, Azure or GCP), virtualization, networking and storage.

Core Competencies

Demonstrates expertise in designing and optimizing AI/ML solutions, with a strong focus on large-scale system architecture and performance engineering. Proficient in integrating AI technologies within media production environments while mentoring engineering teams. Highest-signal resume keywords - AI/ML Solution Development - Large-Scale ML System Design - Cloud Platform Experience (AWS, Azure, GCP) - AI Frameworks (TensorFlow, PyTorch, Hugging Face) - Bilingual Communication (English and French) ATS Optimization Keywords Hard Skills - Machine Learning - Deep Learning - ModelOps - AI Engineering - DevOps - MLOps - Technical Documentation - Cost Efficiency Optimization - Inference Systems Design - GPU Infrastructure Optimization Soft Skills - Mentoring - Collaboration - Communication Industry Keywords - AI Solutions Integration - High-Performance Training Systems - Language Models - Operational Constraints - Scalable Enterprise AI Solutions Tools & Technologies - Docker - Kubernetes - Media Production Platforms (MAM/PAM) - CI/CD Pipelines - Virtualization - Networking - Storage

📌 AI Architect – T & I (Montreal)
🏢 Jobtailor
📍 Montreal