AI Architect – T & I
Summary
Design and optimize large-scale AI infrastructure for a national media organization, focusing on LLM training/inference systems, GPU clusters, and AI integration in media production workflows.
- 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
- 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 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