AI Business Systems Analyst

We are currently seeking an AI Business Systems Analyst to join our team.

The AI Business Systems Analyst defines clear, actionable technical requirements for AI technology capabilities, including Agentic AI systems and supporting technology components. The role applies advanced analysis skills to specify AI behaviors, decision logic, context and data constraints, tool access, and control mechanisms that support enterprise platforms, meet governance and security standards, and perform reliably in production. This role spans analysis and delivery, with accountability for requirements definition, technical design support, quality assurance, and contribution to implementation based on the needs of the initiative. The AI Business Systems Analyst documents interfaces, contracts, rules, failure modes, and expected behaviors, and works closely with AI engineers, architects, and delivery partners to ensure solutions meet functional, technical, quality, security, operational, and supportability expectations.

Here’s what would be included as a part of your typical day

1. AI Analysis: Participates as an active member of the AI Technology team, applying an advanced level of technical expertise to analyze complex problems and assess how AI technologies can solve them. Leads translation of conceptual requirements into technical requirements by producing clear documentation (process maps, workflows, user journeys, specifications, decision tables, and acceptance criteria) that provides direction to makers, testers, and delivery partners.

2. Solution Enablement: Works with leaders and engineers to define implementation‑ready technical requirements for AI technology solutions. Produces specifications that support a range of delivery approaches, automation, custom development, integrations, and AI platforms. Defines requirements for workflows, decision logic, agent behaviors, interfaces, data access patterns, exception handling, logging, monitoring, and operational support needs. Participates in solution design reviews to ensure requirements are technically feasible, align with enterprise architecture and AI governance standards, and support scalability, security, and maintainability.

3. AI Business Solutions collaboration: Liaises with the AI Business Solutions team to understand end‑user needs, usage scenarios, and adoption considerations, and to translate those inputs into clear technical requirements and delivery assumptions. Partners to align AI technology capabilities with user requirements, adoption tactics, and change considerations, while ensuring solution approaches remain feasible, governed, and aligned with enterprise risk, architecture, and supportability standards.

4. Testing, Quality Assurance & Readiness: Plans and leads testing and quality assurance activities, defining and executing test strategies, plans, and scripts for both deterministic and AI‑enabled components. Validates business and technical requirements through demonstrations, scenario‑based and user acceptance testing; supports defect triage and resolution; and ensures solutions are production‑ready with appropriate controls, documentation, support runbooks, and user enablement.

5. Project & Delivery Support: Supports all stages of the initiative life cycle by communicating changes, enhancements, and modifications to project managers and stakeholders; managing risk; and managing/communicating expectations to the team. Contributes analysis and content to business cases and ensures decision-making is supported by evidence and clear options.

6. Stakeholder Relationship Management & Adoption: Builds strong relationships across IT and business groups to support discovery, requirements validation, and adoption. Partners with delivery and business teams to support end‑user orientation and training for new or modified solutions, contributing input and technical context as needed to promote confident and consistent usage.

7. AI Risk, Governance & Controls: Identifies and documents AI‑related risks, assumptions, and constraints, and ensures requirements and solutions align with enterprise risk, security, privacy, regulatory requirements and AI governance standards. Defines appropriate controls, defined autonomy boundaries with human supervision and override mechanisms, auditability, and operational safeguards, and partners with risk, compliance, and technology stakeholders to support responsible, supportable, and compliant AI solutions.

8. Team Support: Participates in regular department and team meetings to discuss current activities and provide advanced input on issues and risks. Provides timely status updates, proactively flags risks, and provides proxy coverage for other roles as required.

Required Skills, Experience & Qualifications

• Bachelor’s degree in IT or an equivalent stream required; additional post‑graduate certification in Business Systems Analysis (or equivalent) preferred.
• 3+ years of related IT experience in a business analysis function (or an equivalent combination of education and experience) required.
• Experience contributing to the design and delivery of enterprise‑grade solutions leveraging AI technology components at scale.
• Experience within the financial services industry is an asset.
• Advanced understanding of delivery life cycles for AI, data, and automation initiatives, with the ability to provide informed input at key decision points.
• Advanced communication and interpersonal skills to facilitate consensus building on business/technical requirements and solutions through collaboration with multiple stakeholders.
• Solid organizational skills to carry out assignments to meet business requirements and deadlines.
• Solid research, analytical, and problem-solving skills to actively participate in development of complex business solutions.
• Ability to translate AI concepts into clear, testable requirements, including controls, auditability, and operational readiness.
• Strong communication skills with ability to communicate complex messages and teach new concepts.
• Solid knowledge of end‑to‑end delivery life cycles for AI and data platforms, from requirements and design through implementation, testing, deployment, and support.
• Solid knowledge of the concepts, theories, practices, and techniques of the information technology field with emphasis on business systems analysis.
• Ability to elicit requirements to build AI enabled experiences, including data access considerations, security constraints, and success measures.
• Comfort partnering with engineering teams to document technical requirements and integration needs.
• Displays an understanding of risk and risk ownership by being able to demonstrate adherence to policies and procedures.