• 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

• Bachelors or masters 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.

Core Competencies

Demonstrates expertise in designing and optimizing AI/ML solutions, with a solid 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