CoreFactor is searching for a Manager, AI Platforms on a permanent/full-time basis for a client in the GTA.
This position is hybrid and will require the successful incumbent to be in the office two (2) days per week.
Purpose
- Develop, execute and maintain a comprehensive enterprise AI platform strategy and capabilities to support the secure, scalable and responsible adoption of artificial intelligence across the organziation. Lead the planning, design, delivery, operation and continuous improvement of enterprise AI and data platforms, including generative AI, machine learning, enterprise search, AI agents and AI-enabled applications.
- Collaborate with cross-functional teams to define business and technical requirements, establish reusable platform capabilities and integrate enterprise data, applications and third-party AI services. Provide leadership in establishing AI platform standards, governance controls, engineering practices, operational processes, performance measures and organization-wide adoption of approved AI technologies.
- The role will lead a multidisciplinary team and work closely with Enterprise Architecture, Cybersecurity, Data Governance, Privacy, Infrastructure, business technology teams and external delivery partners to ensure AI platforms are secure, reliable, governed, cost-effective and aligned with business objectives.
Duties and Responsibilities
- Develop, execute and maintain a complete enterprise AI platform strategy and roadmap to support artificial intelligence, generative AI, machine learning and advanced data capabilities across the organization.
- Partner with cross-functional stakeholders to scope AI platform requirements and build secure, scalable and reusable solutions addressing business and technology needs.
- Establish platform architecture, technical standards and reference patterns for generative AI, retrieval-augmented generation, enterprise search, AI agents, model integration and AI-enabled applications.
- Facilitate, educate and promote the usage and adoption of approved AI platforms, tools, processes and procedures through formal training, continual coaching and consultation.
- Manage, support and develop AI platform and data services staff while promoting technical growth, accountability and engineering excellence.
- Coordinate AI platform planning and delivery across internal employees, contractors, strategic vendors and system integration partners.
- Establish platform priorities, delivery roadmaps, performance measures and service expectations aligned with enterprise objectives.
- Guide and facilitate the resolution of platform, integration, performance, security and operational issues.
- Provide leadership on AI platform best practices and partner with business and technology teams to evaluate platform capability and adoption needs.
- Communicate AI platform strategy, standards, procedures, roadmaps and approved technology patterns across the organization.
- Enforce compliance with internal architecture, cybersecurity, privacy, data governance, Responsible AI and technology management policies and procedures.
Develop and Operate Enterprise AI Platforms
- Lead the design, development, deployment and operation of enterprise capabilities supporting generative AI, machine learning, enterprise search, conversational AI, AI agents and AI-enabled applications.
- Lead the evolution and operational management of enterprise data and AI platforms, including Snowflake, Microsoft Azure AI, Microsoft Copilot and related services.
- Define reusable platform services and integration patterns for connecting enterprise applications, data platforms, APIs, AI models and approved third-party AI providers.
- Establish capabilities for large language model integration, retrieval-augmented generation, prompt orchestration, model routing, enterprise grounding and response validation.
- Enable secure and governed access to enterprise information while preserving platform-native identity, authorization and data-access controls.
- Guide experimentation, proofs of concept and pilot solutions through appropriate processes toward secure and supportable production deployment.
- Promote reuse of common platform capabilities and approved patterns to reduce duplication, technology proliferation and isolated AI solutions.
- Monitor emerging AI technologies and recommend their appropriate use based on business value, security, architectural alignment, operational readiness and total cost.
Platform Consumption and Cost Management
- Establish cost-management and financial operations practices for enterprise AI, cloud and data platforms.
- Monitor and optimize Snowflake consumption, cloud infrastructure usage, AI model consumption, licensing and third-party service costs.
- Develop demand, capacity and cost forecasts to support platform investment and budget decisions.
- Establish reporting and controls to identify unexpected consumption, inefficient workloads and cost-optimization opportunities.
- Evaluate licensing, consumption and commercial models for strategic AI and data-platform technologies.
- Partner with Procurement, Finance, vendors and technology stakeholders to support contract planning, renewals and platform investment decisions.
Platform Architecture and Engineering
- Support and collaborate with Enterprise Architecture and other technology teams to define the structures, services and integration patterns required to support enterprise AI workloads.
- Establish engineering standards for application programming interfaces, model integration, data retrieval, orchestration, identity and access management, logging and monitoring.
- Establish platform engineering practices including infrastructure as code, continuous integration and continuous deployment, automated testing, environment management and release management.
- Define development, test and production environment strategies supporting secure and controlled AI solution delivery.
- Guide technical teams in developing scalable, maintainable and supportable AI platform components.
- Ensure platform architecture supports interoperability with enterprise applications, Snowflake, Microsoft technologies, data services and approved external AI providers.
- Maintain technical roadmaps and support architecture decisions concerning platform selection, capability development, integration and technology lifecycle.
Requirements
- Undergraduate degree in Artificial Intelligence, Computer Science, Engineering, Data Science, Information Systems or a related field, or equivalent relevant experience.
- 8–10+ years of progressive technology experience, including leadership experience in AI, data, cloud or enterprise platform environments.
- Strong understanding of modern AI technologies and architectures, including generative AI, large language models, RAG, AI agents and enterprise AI solutions.
- Experience with enterprise data and AI platforms such as Snowflake, Microsoft Azure AI, Microsoft Copilot or comparable technologies.
- Strong understanding of cloud architecture, integration, cybersecurity, identity and access management, AI governance and Responsible AI practices.
- Demonstrated experience leading technical teams and managing internal resources, contractors, vendors and strategic delivery partners.
- Strong strategic thinking and ability to translate business requirements into scalable enterprise technology capabilities and roadmaps.
- Excellent communication, stakeholder management and problem-solving skills, with the ability to influence senior business and technology leaders.
