Tech Lead — Enterprise AI Platform & Data

Air Canada; Toronto; On-site; CAD 140,000 - 190,000; Full time

Job summary

Air Canada seeks a Tech Lead, Data & AI to own and evolve the Enterprise-Grade Agentic Platform, translating business and security requirements into scalable technical designs. You will collaborate across Data & AI, Enterprise Architecture, Cybersecurity, AI Governance, and platform teams to deliver reusable components, observability, and robust testing—driving AI product lifecycle maturity.

You will implement Knowledge Models, knowledge graphs, and retrieval patterns while ensuring security,

Qualifications

• Degree in Engineering, Computer Science or Mathematics/Statistics.
• Minimum five years of experience in AI, software, data, cloud, or platform engineering in a large enterprise.
• Experience leading design, implementation, integration, or operation of enterprise platforms and shared services.
• Strong knowledge of Agentic AI, generative AI, LLMs, 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.

Responsibilities

• Own the implementation, integration, operation, and evolution of the Enterprise-Grade Agentic Platform.
• Translate requirements into technical designs, engineering backlogs, and milestones.
• Define platform architecture with Enterprise Architecture for modularity, interoperability, scalability, security.
• 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, retrieval patterns.
• Establish reusable domain knowledge models to avoid duplicative layers.
• Implement observability and distributed tracing across agents, models, prompts, retrieval, tools, latency, errors, cost, safety, outcomes.
• Define telemetry, instrumentation, trace, 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, IAM, AI Governance, Privacy, Risk, and Architecture to implement secure, auditable 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 and mentoring.

Skills

AI Platform

Enterprise Architecture

CI/CD

Observability

Security & Governance

Knowledge Graphs

Team Leadership

Azure/AWS

Education

Engineering/Computer Science

Tools

Azure

AWS

Snowflake

Git

Job description

Air Canada seeks a Tech Lead, Data & AI to own and evolve the Enterprise-Grade Agentic Platform, translating business and security requirements into scalable technical designs. You will collaborate across Data & AI, Enterprise Architecture, Cybersecurity, AI Governance, and platform teams to deliver reusable components, observability, and robust testing—driving AI product lifecycle maturity.

You will implement Knowledge Models, knowledge graphs, and retrieval patterns while ensuring security,