Senior Manager, Machine Learning & AI (Digital Strategy And Product) (English Services)

CBC/Radio-Canada•Toronto, ON

8d•Onsite

About The Position

This is an 18-month contract position where the incumbent will play a central role in shaping the AI strategy and driving the development and deployment of advanced AI/ML solutions. As the Manager of Machine Learning and AI, the role involves leading two talented, multidisciplinary teams of Machine Learning Developers, Data Scientists, Data Engineers, AI Engineers, and Software Developers. Collaboration with Product and Technical leaders is key to ensure seamless integration of AI/ML solutions into the product ecosystem. The role is responsible for empowering teams to deliver impactful products that drive business outcomes and create a superior audience experience.

Requirements

At least seven years of experience in software or machine learning.
A minimum of three years in a leadership role, managing teams of data scientists, ML or software developers.
Proven track record of building and scaling AI platforms that successfully deploy ML models into production.
Deep understanding of ML and deep learning frameworks like PyTorch and TensorFlow.
Extensive experience with MLOps, CI/CD pipelines, containerization, and major cloud platforms (AWS, Azure, or GCP).
Skilled in data modeling and building robust data pipelines.
Solid grasp of core AI concepts, including NLP, Generative AI, and LLMs.
Product-focused mindset and comfortable working within a digital development organization using agile methodologies.
Ability to translate complex business needs into technical solutions and drive product strategy.
Exceptional communicator who excels at leading, mentoring, and growing a team.
Ability to foster a collaborative and innovative environment.
Skilled at communicating effectively with diverse stakeholders.
Forward-looking, adaptable leader who thrives in a fast-paced environment.
Passionate about innovation, continuous learning, and can navigate ambiguity with ease.
Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, Machine Learning, or a related technical field.
Candidates may be subject to skills and knowledge testing.
A mandatory Criminal record check.
Other background checks may be conducted based on the operational requirements of the position.
Adherence to the Code of Conduct.
Informing about any situation that constitutes or could appear to constitute a conflict of interest.

Responsibilities

Lead, mentor, and scale two high-performing engineering teams, fostering a culture of trust, innovation, and continuous learning.
Empower team members (ML Developer, Data Scientist, Data Engineer, AI Engineer, and Software Developers) to achieve excellence and drive innovation in AI/ML initiatives.
Ensure best practices in engineering for scaling ML-powered features.
Act as a thought leader on the application of AI within the enterprise's industry, providing guidance on the overall AI/ML strategy.
Contribute to and influence the AI/ML product roadmap based on feedback from audiences and emerging trends.
Guide teams in developing and implementing an AI Engineering platform that enables the scalable deployment of machine learning models.
Direct the team's efforts in creating and optimizing MLOps pipelines to ensure continuous deployment, integration, and monitoring of machine learning models, prioritizing scalability, efficiency, and cost-effectiveness.
Oversee the entire lifecycle of AI software components, from development and testing to deployment and ongoing support, focusing on areas like foundation model training, LLM inference, and implementing robust guardrails.
Provide direction on building, deploying, and supporting secure AI software, ensuring fairness, transparency, and ethical considerations are integrated throughout the development lifecycle.
Guide the design, development, and implementation of AI models into operational workflows, with a focus on security and compliance.
Partner and collaborate with product, architecture, quality engineering and infrastructure to define and execute the Product roadmap.
Communicate complex ideas clearly and effectively across disciplines and to diverse stakeholders, including senior leadership.