About the role
Being part of Air Canada is to become part of an iconic Canadian symbol, recently ranked the best Airline in North America. Let your career take flight by joining our diverse and vibrant team at the leading edge of passenger aviation.
The Tech Lead, Data & AI is the hands-on technical owner responsible for implementing, integrating, and operating Air Canada’s Enterprise-Grade Agentic Platform. The role translates enterprise AI strategy, architecture, governance, and business requirements into a modular, secure, observable, and reusable platform that enables AI products and agentic solutions to be deployed and operated consistently across the enterprise.
The immediate focus of the role is to establish and integrate three foundational platform capabilities: the Knowledge Model for AI, including enterprise knowledge graph, semantic, ontology, metadata, lineage, and retrieval patterns; AI Observability and Tracing, providing end-to-end visibility into agent, model, prompt, retrieval, and tool execution; and Testing and Evaluation, providing standardized pre-production testing, continuous production evaluation, quality controls, and evidence-based release gates. The Tech Lead will work across Data & AI, Enterprise Architecture, Cybersecurity, AI Governance, Digital Products, Software and Platform Development, and external partners to ensure these capabilities operate as shared enterprise services rather than isolated, platform-specific solutions.
Responsibilities
Technically own the implementation, integration, operation, and evolution of the Enterprise-Grade Agentic Platform.
Translate business, architecture, security, and governance requirements into technical designs, engineering backlogs, and delivery milestones.
Define the platform architecture with Enterprise Architecture, ensuring modularity, interoperability, scalability, security, and alignment with Air Canada standards.
Integrate AI platforms, agent frameworks, gateways, registries, knowledge services, observability tools, and evaluation capabilities.
Implement the Knowledge Model for AI, including knowledge graphs, ontologies, semantics, metadata, provenance, lineage, and retrieval patterns.
Establ…
