VP, AI Engineering

League Inc.•Toronto, ON

5h•CA$265,000 - CA$315,000•Hybrid

About The Position

League is seeking a VP, AI Engineering to lead the technical vision, architecture strategy, and operational execution of our next-generation AI and data ecosystem. Reporting into senior technology leadership, this role sits at the intersection of AI engineering, healthcare intelligence, and enterprise architecture. You will be responsible for defining how League’s AI-native platform capabilities are architected, governed, scaled, and operationalized across our healthcare ecosystem. This is a deeply strategic and highly technical leadership role. You will guide the evolution of League’s AI and data architecture strategy while partnering closely with Engineering, Product, Security, Data Science, Infrastructure, and Go-to-Market leadership to ensure scalable, secure, and high-performing systems that power consumer healthcare experiences for millions of members.

Requirements

15+ years of progressive software engineering, data platform, or AI systems experience, including significant leadership experience in enterprise-scale environments.
Proven track record leading large-scale AI, data, or platform modernization initiatives in complex SaaS or healthcare technology organizations.
Deep experience architecting and operationalizing cloud-native AI and data systems at scale.
Experience leading highly cross-functional technical organizations through periods of transformation, ambiguity, and rapid growth.
Strong background in enterprise architecture, AI platform strategy, distributed systems, and operational engineering governance.
Experience building or scaling AI-native engineering organizations and workflows.
Healthcare, life sciences, payer/provider, or regulated enterprise platform experience strongly preferred.
Experience operating in highly matrixed organizations with executive stakeholder management responsibilities.
Strong operational leadership capability balancing delivery, platform stability, scalability, and organizational effectiveness.
AI-native architectures
Agentic systems and orchestration frameworks
ML platform architecture
Vector databases and retrieval systems
AI evaluation and observability systems
Enterprise analytics and operational intelligence platforms
Data governance and lineage systems
AWS, GCP, and/or Azure
Cloud-native platform architectures
Distributed systems
Event-driven systems
Microservices
Kubernetes and containerized infrastructure
CI/CD and platform engineering practices
Scalable SaaS platform architectures
API ecosystems and interoperability
Data-intensive systems
Enterprise integration patterns
System resiliency and reliability engineering
Performance optimization and observability
AI-assisted software development workflows
Intelligent automation systems
Developer enablement platforms
Engineering productivity optimization
AI governance and responsible AI practices
Bachelor’s degree in Computer Science, Engineering, Computational Science, Data Science, or related technical discipline required.
Demonstrated experience using AI tools in a practical, responsible way
Curiosity and openness to experimenting with new technologies
Ability to balance efficiency with quality and sound judgment
Ensure access management is performed in compliance with the employee's role and responsibilities
Responsibility and accountability for executing League's policies and procedures within the department/ team
Notification of HR, Legal, Compliance & Security of any incidents, breaches or policy violations
Compliance with Information Security Policies

Nice To Haves

Use AI tools as part of your daily workflow to enhance productivity, problem-solving, and decision-making (e.g., drafting, analysis, coding, research, or process automation)
Apply judgment and accountability when using AI by reviewing outputs for accuracy, bias, and quality before use
Continuously learn and adapt as new AI tools and capabilities emerge, incorporating them into your ways of working
Identify opportunities to improve how work gets done from personal productivity to team-level workflows by leveraging AI effectively
Operate with strong data responsibility and security awareness, especially when working with sensitive or regulated information

Responsibilities

Define and evolve League’s enterprise AI and data architecture strategy across platform, product, and operational systems.
Lead architectural modernization initiatives focused on cloud-native AI systems, data orchestration, and scalable healthcare intelligence platforms.
Establish architectural standards, governance frameworks, and engineering guardrails across AI and data ecosystems.
Drive technical strategy for AI-native platform capabilities, including agentic workflows, orchestration systems, vectorized data systems, and intelligent automation frameworks.
Partner closely with Engineering and Product leadership to ensure architectural decisions align with business priorities, customer commitments, and long-term platform scalability.
Champion AI-native engineering practices across the organization, enabling teams to effectively leverage AI-assisted development, intelligent automation, and agentic tooling in day-to-day workflows.
Build scalable frameworks for AI-enabled delivery, experimentation, evaluation, observability, and operational governance.
Guide adoption of emerging AI technologies and establish standards for responsible, secure, and compliant AI implementation in healthcare environments.
Partner with Engineering leadership to improve developer productivity, architectural consistency, and delivery velocity through AI-assisted engineering systems.
Lead the modernization of League’s data ecosystem into scalable, cloud-native architectures supporting analytics, machine learning, operational intelligence, and member personalization.
Define strategies for data interoperability, healthcare data exchange, identity resolution, event-driven architectures, and platform resiliency.
Partner with Security, Compliance, and Infrastructure teams to ensure systems meet HIPAA, PIPEDA, SOC2, and enterprise healthcare compliance requirements.
Drive architectural decisions balancing performance, scalability, reliability, security, and operational simplicity.
Build and operationalize engineering governance models for AI and data platform delivery.
Establish KPIs and operational metrics around platform reliability, delivery predictability, AI model performance, system scalability, and engineering effectiveness.
Lead prioritization and execution across highly cross-functional environments with competing priorities and organizational complexity.
Navigate ambiguity and make pragmatic architectural trade-offs in rapidly evolving technical and business environments.
Serve as a strategic advisor to executive leadership on AI transformation, platform scalability, and enterprise data strategy.
Partner cross-functionally with Product, Engineering, Security, Compliance, Customer Delivery, and Go-to-Market leaders.
Translate complex technical concepts into business-aligned recommendations for non-technical stakeholders and executive audiences.
Mentor and develop senior engineering and architecture leaders across the organization.