About the role

AI Architect designing scalable LLM and multimodal AI infrastructure for CBC/Radio-Canada’s public-service media platforms. Integrating secure AI systems across production workflows, studios and GPU clusters.

Responsibilities

Design and build high-performance training and inference systems for LLMs and multimodal AI models
Optimize end-to-end training through high-throughput data pipelines, distributed training and mixed precision
Optimize inference and serving engines using KV caching, batching, quantization and long-context processing
Collaborate with T&I to design, right-size and evolve the internal GPU cluster
Architect AI integration within CBC/Radio-Canada media production environments, including PAM, MAM, television and radio studios
Evaluate and standardize MCP and A2A integration protocols for connecting models with Avid, Dalet and Adobe platforms
Develop secure video understanding systems for file-based and IP live workflows using codecs, wrappers and SMPTE-2110
Provide expert guidance to improve architectural scalability, latency and reliability
Mentor engineers and data scientists in large-scale ML system design and performance engineering
Develop MLOps/LLMOps pipelines focused on observability, performance profiling and automated testing
Explain technological innovations to decision-makers and production teams and influence strategic investments

Bachelor’s or master’s degree in software engineering, information technology, artificial intelligence, mathematics or a related natural science field
Functional bilingualism in 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
Experience with language models and designing solutions optimized for cost efficiency and scale
Strong conceptual understanding of LLM, RAG and AI agent architectures, including frameworks and operational constraints
Experience selecting AI frameworks such as TensorFlow, PyTorch and Hugging Face
Experience selecting cloud platforms such as Azure, AWS and GCP
Experience with orchestration tools such as Docker and Kubernetes
Knowledge of ModelOps, AI engineering, DevOps and MLOps practices, including CI/CD pipelines
Solid understanding of machine learning and deep learning fundamentals
Strong technical documentation skills, including diagrams, demos and technical artifacts
Hands-on experience with media production platforms such as MAM/PAM
Experience designing scalable solutions in highly available, 24/7 environments
Working knowledge of AWS, Azure or GCP, virtualization, networking and storage
Candidates may be subject to skills and knowledge testing
Successful candidates must complete a mandatory criminal record check and other role-specific background checks