AI Architect (T & I) (Telework/Hybrid)

CBC/Radio-Canada•Montreal, QC

4d•Hybrid

About The Position

Technology & Infrastructure (T&I) is the backbone and the future-forward arm of CBC/Radio-Canada. Our purpose is to constantly innovate to evolve and maintain the Corporation’s technology and infrastructure. We are the people that make stuff work. We make connections between media content, systems, people and places. We are the space in between. This is a hybrid position with a mix of in-office and remote work. Work arrangements will be discussed with hiring managers per departmental guidelines. If you want to push the boundaries of media technology and are excited by the challenge of bridging creativity with cutting-edge tech, this role is for you! As a key member of the Architecture and Strategic Development (Media System Architecture) team, you will design and plan CBC/Radio-Canada’s future technology infrastructure. Our mission is to transform our disparate media workflows by integrating hybrid agents, video understanding systems and advanced artificial intelligence architectures operating 24/7. We are looking for an AI Architect with deep technical expertise in optimizing training and inference for large language models (LLMs), multimodal models and AI systems in a media context. As a technical thought leader, you will design robust systems to make AI faster, more scalable and more reliable, ensuring their seamless integration into our critical production ecosystems (PAM, MAM, studios).

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.
Strong 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.

Nice To Haves

Experience with modern video software architectures (e.g., NVIDIA Holoscan for Media) or ultra-high-performance IP transfer protocols.
Demonstrated experience managing complex workflows through cloud or hybrid data pipelines.
Active contributions to open-source high-performance AI projects and published research, or deep understanding of security frameworks for responsible AI.

Responsibilities

Design and build high-performance training and inference systems for LLMs and multimodal AI models.
Optimize end-to-end training, including high throughput data pipelines (streaming, sharding, bucketing), distributed training (parallelism strategies) and mixed precision.
Optimize inference and serving engines to guarantee minimum latency using KV caching design/management, batching, quantization and long-context processing.
Collaborate with the Technology & Infrastructure (T&I) team to design, right-size and evolve our internal GPU cluster into a new-generation infrastructure capable of supporting these massive workloads.
Plan, develop and architect the integration of AI solutions within CBC/Radio-Canada’s media production environments (PAM, MAM, television and radio studios).
Evaluate and standardize integration protocols such as Model Context Protocol (MCP) and agent-to-agent (A2A) architectures to interconnect our models with complex production platforms (Avid, Dalet, Adobe).
Ensure absolute data security while developing video understanding systems that can interact with both file-based workflows (codecs, wrappers) and IP live workflows (SMPTE‑2110).
Provide expert guidance on system efficiency, spearheading initiatives to dramatically improve architectural scalability, latency and reliability.
Mentor and elevate the organization’s engineers and data scientists in large-scale ML system design and performance engineering.
Develop robust MLOps/LLMOps pipelines with a constant focus on observability, performance profiling and automated testing.
Summarize and explain technological innovations to make them understandable to decision-makers and production teams and influence our strategic investments.