AI Operations Leader

Zafin•Toronto, ON

8d•CA$105,000 - CA$190,000•Hybrid

About The Position

Zafin is an AI platform company helping regulated institutions modernize how critical work is designed, governed, and delivered. Our technology enables organizations to move faster while maintaining the governance, accountability, and control required in highly regulated environments. Our portfolio includes Zafin AIOS, an agent orchestration platform for governed AI work; the Zafin Banking Platform, which helps banks modernize product, pricing, offers, billing, loyalty, and relationship management; and Zafin IO, an integration platform that connects data, systems, and workflows across complex enterprise environments. Headquartered in Toronto, Canada, Zafin partners with leading financial institutions across North America, Europe, the Middle East, Africa, and Asia-Pacific. As AI transforms the future of financial services, we're building the platforms that help regulated organizations adopt AI responsibly and at scale. The AI Operations Leader leads the shared operations function that provides AI agent configuration, evaluation, and knowledge support to client-facing squads. The role ensures these services are delivered consistently, efficiently, and at the quality required to enable AI-native delivery without interruption and across multiple client engagements. The role manages the people, capacity, operating processes, and service standards that allows the Embed and Expand phases of AIOS (Zafin’s AI Operating System) delivery lifecycle to scale effectively. It ensures client-facing squads can access reusable AI agents, prompts, integrations, knowledge assets, evaluation support, and tooling rather than duplicating effort or creating delivery bottlenecks. Working closely with client-facing squads, the role aligns operational capacity with delivery demand, manages service quality and performance, resolves cross-team dependencies, and drives continuous improvement across the shared operations function. The role is also for team performance, workload planning, service consistency, operational metrics, escalation management, and the adoption of shared AIOS assets and ways of working. Through disciplined operations, measurable service outcomes, and continuous improvement, the role helps AIOS scale reliably across client engagements.

Requirements

Typically, 6–12 years of experience in technical operations, shared services management, engineering operations, delivery management, or related fields, including demonstrated people leadership and accountability for operational performance across multiple teams or stakeholders.
Experience leading multidisciplinary or distributed teams responsible for technical support, configuration, quality, knowledge, or delivery services.
Experience managing demand, capacity, priorities, and service quality across multiple concurrent initiatives.
Experience using operational metrics and continuous-improvement practices to improve service delivery, responsiveness, and quality.
Degree in Comp Sci, Comp Eng, Software Engineering, Business Operations, Information Systems, or related field, or equivalent experience.
Strong understanding of shared-services operations, demand and capacity planning, workload prioritization, service delivery, and operational performance management.
Working knowledge of AI agent configuration, AI evaluation engineering knowledge operations, and delivery lifecycle, of AI-enabled solutions.
Experience establishing service standards, operational metrics, escalation processes, and continuous-improvement practices with strong process and quality-assurance orientation.
Ability to use operational data, dashboards, and trend analysis to make capacity, prioritization, and improvement decisions.

Nice To Haves

Experience managing distributed or offshore teams.
Experience managing operations within a professional services, technology delivery, platform enablement, or shared-services organization.
Experience supporting AI, machine learning, agentic AI, or automation delivery environments.
Experience managing distributed or offshore teams across multiple time zones.
Experience working with centers of excellence or matrixed functional teams to secure and coordinate specialized resources.
Experience implementing operational dashboards, service metrics, workflow automation, or capacity-planning tools.
Experience reducing turnaround time, operational rework, or duplicated delivery effort.

Responsibilities

Ensure AI agent configuration, evaluation, and knowledge support services are delivered efficiently, consistently, and in accordance with defined service and quality expectations across client-facing squads.
Partner with client-facing squads to understand delivery demand, forecast support requirements, establish priorities, and align shared operations capacity with business and client commitments.
Establish clear intake, prioritization, assignment, escalation, and service-management processes for work entering the Agent Operations Support Squad.
Monitor service delivery, identify emerging bottlenecks or demand-capacity gaps, and take corrective action before client-facing delivery is affected.
Lead workload planning, demand forecasting, resource allocation, and capacity management across the Agent Operations Support Squad, balancing delivery priorities, available skills, and service commitments.
Maintain visibility into team capacity, utilization, work in progress, skill coverage, and future demand, and communicate risks and trade-offs to Delivery and Platform leadership.
Manage the operational adoption, maintenance, and consistent use of shared AI agents, prompts, integrations, knowledge assets, evaluation assets, tooling, and the shared ways-of-working across teams.
Set clear objectives, accountabilities, service expectations, and development priorities for team members.
Build cross-disciplinary capability through coaching, succession planning, knowledge sharing, and targeted skill development.
Create an inclusive, accountable, and high-performing team culture focused on service quality, collaboration, and continuous improvement.
Identify opportunities to consolidate duplicated work, improve reuse, and transition repeatable delivery activities into shared operational services.
Define, report on, and monitor operational service measures, including turnaround time, backlog health, capacity utilization, service quality, rework, demand trends, adoption of shared assets, and stakeholder satisfaction.
Maintain regular evaluation and AI agent optimization rhythm, and the monthly governance review, for supported squads, ensuring actions, risks, decisions, and improvement priorities are tracked through completion.
Identify and escalate capacity, quality, dependency, and service delivery risks to the appropriate leadership before they affect client-facing delivery.