- 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




- Robust 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

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