Tech Lead, Data & AI

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.
• Establish reusable, linked domain knowledge models that avoid monolithic or duplicative knowledge layers.
• Implement observability and distributed tracing across agents, models, prompts, retrieval, tools, latency, errors, cost, safety, and business outcomes.
• Define common telemetry, instrumentation, trace, session, dashboard, alerting, and diagnostic standards.
• Implement shared Testing and Evaluation capabilities for pre-production testing, release gating, regression and safety testing, and continuous production evaluation.
• Establish patterns for evaluation datasets, synthetic tests, automated evaluators, deterministic checks, human review, quality thresholds, and evidence retention.
• Embed testing, evaluation, observability, and quality controls into CI/CD and AI-product lifecycle processes.
• Define technical acceptance criteria and validate platform capabilities, integrations, and partner deliverables.
• Develop reusable components, APIs, SDKs, templates, reference implementations, and infrastructure-as-code patterns.
• Support low-code, pro-code, embedded, standalone, and custom agentic solutions across multiple technologies.
• Partner with Cybersecurity, Identity and Access Management, AI Governance, Privacy, Risk, and Architecture to implement secure, auditable, and responsible AI controls.
• Coordinate with cloud, infrastructure, networking, DevOps, and platform teams to provision and operate required environments and services.
• Monitor and optimize platform reliability, performance, scalability, availability, resource use, and cost.
• Lead incident investigation, root-cause analysis, and remediation for shared platform capabilities.
• Maintain the technical roadmap, architecture decisions, integration standards, operational documentation, and support procedures.
• Provide technical direction to internal teams, contractors, and partners, while supporting onboarding, knowledge transfer, mentoring, and sustainable internal ownership.

Qualifications

• Degree in Engineering, Computer Science or Mathematics/Statistics.
• Minimum five years of experience in AI, software, data, cloud, or platform engineering within a large enterprise.
• Experience leading the design, implementation, integration, or operation of enterprise platforms and shared services.
• Strong knowledge of Agentic AI, generative AI, large language models, orchestration, retrieval-augmented generation, tool integration, and AI-product lifecycle practices.
• Knowledge of Azure, AWS, Snowflake, and other AI-driven platforms.
• Experience with knowledge graphs, ontologies, semantic models, metadata, lineage, or knowledge curation.
• Knowledge of AI observability, telemetry, distributed tracing, monitoring, diagnostics, performance, and cost management.
• Experience with AI testing and evaluation, including evaluation datasets, automated evaluation, regression, safety testing, human review, and release controls.
• Strong knowledge of cloud-native architecture, APIs, identity and access management, CI/CD, infrastructure as code, cybersecurity, and responsible AI.
• Proven ability to exhibit sound judgement, keen eye for details and tenacity for solving difficult problems.
• Proven capability to gather, manage and synthesize large amounts of information efficiently, effectively and creatively with attention to detail.
• Ability to work cooperatively with others on a team and be able to effectively drive cross-team solutions that have complex dependencies and requirements.
• Must possess strong communication (verbal and written), research and interpersonal skills as well as excellent time-management and organizational skills, with a proven ability to be highly productive and efficient in a team driven environment.
• Must have strong problem-solving and analytical skills.
• Experience in Airline AI platform management is an asset
• Demonstrate punctuality and dependability to support overall team success in a fast-paced environment.

Personal Attributes

• Combines hands-on technical depth with an enterprise platform perspective.
• Inspires and energizes engineering teams with a high level of enthusiasm, fostering a culture of excellence and ownership.
• Demonstrates a forward-thinking, innovative mindset—constantly exploring emerging technologies and unconventional solutions to complex challenges.
• Builds and sustains a culture of trust, transparency, and psychological safety, enabling open dialogue and empowering diverse perspectives.
• Challenges assumptions and the status quo with curiosity and courage, driving continuous improvement and elevating team performance.
• Promotes interoperability, reuse, standardization, security, and sustainable engineering practices.
• Leads by example with integrity, resilience, and a commitment to mentoring others toward their full potential
• Demonstrates ownership, sound judgement, curiosity, and disciplined execution.

Conditions of Employment

Candidates must be eligible to work in the country of interest at the time any offer of employment is made and are responsible for obtaining any required work permits, visas, or other authorizations necessary for employment. Prior to their start date, candidates will also need to provide proof of their eligibility to work in the country of interest.

Linguistic Requirements

Based on equal qualifications, preference will be given to bilingual candidates.

Diversity and Inclusion

Air Canada is strongly committed to Diversity and Inclusion and aims to create a healthy, accessible and rewarding work environment which highlights employees’ unique contributions to our company’s success.

As an equal opportunity employer, we welcome applications from all to help us build a diverse workforce which reflects the diversity of our customers, and communities, in which we live and serve.

Air Canada thanks all candidates for their interest; however only those selected to continue in the process will be contacted.