Director of AI
Team: Data
Location: Toronto, Vancouver
Commitment: Full-Time Permanent
Workplace Type: remote
RESPONSIBILITIES
- Act as the AI, LLM, and Data Science SME on client engagements, supporting discovery, solution design, and delivery activities.
- Lead technical discussions with client stakeholders to assess AI readiness, data availability, architectural considerations, and feasibility.
- Design and guide AI solution approaches, including: Large language model–based applications (e.g., retrieval-augmented generation, orchestration, evaluation). Applied machine learning and data science workflows. Integration with enterprise data platforms and digital systems.
- Collaborate with data engineers, platform engineers, and application teams to ensure AI solutions align with cloud-native architectures and delivery standards.
- Support the progression of AI initiatives from early experimentation into implementation phases suitable for broader organizational adoption.
- Advise on evaluation, monitoring, and operational considerations for AI and machine learning solutions.
- Embed responsible AI, privacy, security, and compliance considerations into solution design and delivery.
- Partner with cross foundational teams to support proposals, discovery workshops, and active client engagements.
- Contribute to reusable AI patterns, approaches, and documentation that can be leveraged across multiple clients.
- Stay current with emerging AI, LLM, and data science techniques, applying new capabilities where they provide clear value.
REQUIREMENTS
- 8+ years of experience working with AI, data science, or machine learning in enterprise or consulting environments.
- Demonstrated experience acting as a subject matter expert on complex, client-facing technical initiatives.
- Master’s degree in Artificial Intelligence, Computer Science, Data Science, Machine Learning, or a related field is required.
- Strong understanding of modern AI approaches, including large language models and applied machine learning.
- Experience designing AI solutions that integrate with enterprise data platforms and cloud-native architectures.
- Exposure to MLOps or LLMOps practices, including deployment, monitoring, and lifecycle management.
- Familiarity with data science workflows, model evaluation, and performance trade-offs.
- Solid understanding of data privacy, security, and compliance considerations related to AI systems.
- Ability to communicate complex technical concepts clearly to both technical and non-technical audiences.
- Strong analytical and problem-solving skills, with sound judgment in balancing innovation, risk, and practicality.
NICE TO HAVES
- Experience working in regulated or large-scale enterprise environments.
- Experience contributing to internal standards, playbooks, or reusable delivery frameworks is a plus.
- Professional certifications in GCP, Snowflake, or Databricks are considered a plus.
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