AI Solutions Engineer - Technology Workplace Services

Join a Challenger

At EQ, we're remaking banking so every Canadian gets ahead, every day. Serving nearly 4 million Canadians from coast to coast, we offer a wide variety of financial services from banking and lending, to trust and credit union solutions. 

We've been at this since 1970, challenging the conventions of traditional banking with smarter, faster, and more connected financial experiences.

What's kept us moving?  The people behind it all: challengers who ask better questions, push back on old assumptions, and look for a better way forward.

If you're driven to help reshape how banking works for Canadians and the businesses that power our economy, this could be your next big opportunity. 

We can’t wait to get to know you! 

The Core Responsibilities!

AI Enablement, Business Analysis & Process Improvement (30%)

Partner with TWS and business stakeholders to identify, assess, prioritize, and document high-value AI, automation, and process-improvement opportunities.

Analyze current-state workflows, pain points, controls, dependencies, exception paths, data requirements, and measurable improvement objectives.

Determine whether AI, workflow automation, scripting, process redesign, or a non-technical change is the most appropriate response to a business or operational need.

Translate business requirements into process designs, solution requirements, acceptance criteria, test scenarios, implementation plans, support models, and measurable success criteria.

Develop use cases for ticket summarization, triage, routing, response drafting, knowledge retrieval, trend analysis, and service-quality checks.

Maintain a prioritized AI and automation pipeline aligned with TWS objectives, capacity, risk tolerance, and measurable business value.

Gather user feedback, identify adoption barriers, and recommend process or solution improvements that increase usability and sustained value.

AI Engineering & Solution Development (40%)

Design, configure, develop, test, deploy, maintain, and support secure, scalable, and maintainable AI-powered copilots, agents, automations, scripts, analytics, and low-code solutions for TWS and digital workplace use cases.

Build proof-of-concepts, prototypes, and production-ready solutions using approved platforms such as Microsoft Copilot Studio, Azure AI Foundry, Microsoft Fabric, Power Platform, APIs, PowerShell, Python, and other sanctioned technologies.

Develop enterprise search, retrieval, grounding, orchestration, agent, and workflow solutions using approved TWS and enterprise knowledge sources.

Create reusable prompts, workflows, templates, connectors, scripts, integrations, and solution components to improve consistency and accelerate delivery.

Integrate AI capabilities with Microsoft 365, ServiceNow, HR platforms, collaboration tools, endpoint, asset, identity, reporting, knowledge, and operational workflow systems.

Define grounding sources, data mappings, permission boundaries, validation rules, structured outputs, and controls so solutions are accurate, traceable, and fit for purpose.

Apply approved engineering practices for environment management, configuration, credentials, source control, testing, release, monitoring, logging, troubleshooting, recovery, rollback, and retirement.

Complete functional, security, privacy, accessibility, regression, and user acceptance testing before production release.

Create and maintain technical runbooks covering ownership, dependencies, access, recovery, rollback, incident response, monitoring, support handoff, and retirement.

Collaborate with Architecture, Security, Privacy, Risk, Service Management, Data, platform teams, and vendors to validate designs and address gaps before implementation.

AI Governance, Risk & Responsible Use (15%)

Ensure AI and automation solutions comply with approved architecture, security, privacy, data governance, records-management, acceptable-use, change-management, audit, accessibility, and regulatory requirements.

Perform preliminary risk and impact assessments and engage Information Security, Privacy, Risk, Compliance, Data, Architecture, or other control partners as required.

Implement responsible AI controls, including access management, data protection, auditability, output validation, exception handling, escalation, rollback, and human oversight.

Document solution purpose, users, data inputs, processing logic, outputs, limitations, risks, dependencies, controls, approvals, testing, changes, exceptions, incidents, and periodic reviews.

Ensure AI-generated outputs and automated actions are validated and are not used as the sole basis for material operational or access decisions.

Escalate material risks, control failures, data exposure, inaccurate outcomes, production issues, and high-priority service impacts to the Manager, TWS and appropriate partners.

Knowledge Management, Adoption & Continuous Improvement (5%)

Develop and maintain AI-assisted knowledge articles, SOPs, troubleshooting guides, prompt guidance, playbooks, standards, reference architectures, job aids, and support documentation.

Validate AI-assisted content for accuracy, clarity, accessibility, currency, and alignment with approved processes before publication.

Build AI literacy and adoption through demonstrations, workshops, training, coaching, knowledge sharing, enablement programs, and office-hour support.

Advise teams on AI best practices, prompt engineering, output validation, data handling, responsible use, solution design, and automation opportunities.

Monitor solution accuracy, reliability, security, privacy, cost efficiency, adoption, exceptions, user satisfaction, and business outcomes; implement improvements based on evidence and feedback.

Develop dashboards, metrics, and reporting that demonstrate adoption, quality, productivity gains, automation savings, service improvements, risk reduction, and operational impact.

Technology Workplace Services Technology Support & Operational Continuity (10%)

Provide targeted support for endpoints, collaboration tools, audio/visual equipment, enterprise applications, and related workplace technology services when required.

Use operational knowledge to validate that AI-enabled improvements function effectively within TWS support processes and meet user needs.

Manage assigned incidents, requests, problems, and changes in accordance with ITIL practices, documented procedures, service expectations, and escalation requirements.

Assist with onboarding, offboarding, device deployment, software rollout, inventory, and other TWS activities during priority events or coverage needs.

Monitor and respond to production issues affecting AI-enabled workflows, automations, or related TWS services.

Let's Talk About You!

College diploma or university degree in Information Technology, Computer Science, Business Technology, Data and Analytics, or a related discipline, or equivalent experience.
5 or more years of experience in IT service delivery, workplace technology, AI enablement, process improvement, automation, business analysis, or a related technical role.
Practical experience analyzing requirements and designing, documenting, testing, deploying, monitoring, and supporting AI, automation, scripting, analytics, or low-code solutions.
Working knowledge of generative AI and solution patterns, including prompt design and orchestration, retrieval-augmented generation, enterprise search, grounding, structured output, tool or function calling, evaluation, and human-in-the-loop controls.
Familiarity with Microsoft 365 and approved enterprise AI and automation platforms, including Microsoft Copilot, Copilot Studio, Power Automate, Power Apps, Power BI, Microsoft Fabric, Azure AI, PowerShell, APIs, and related tools used by the Bank.
Knowledge of API integration, authentication and authorization, managed identities or service accounts, secrets management, data transformation, secure connector design, source control, solution packaging, environment configuration, release management, monitoring, logging, and technical runbooks.
Understanding of ITIL practices, including Incident, Request, Problem, Change, Knowledge, Asset, and Service Level Management.
Strong knowledge of security, privacy, data lifecycle, governance, classification, minimization, access, retention, lineage, appropriate use, and AI evaluation principles.
Strong analytical, troubleshooting, documentation, communication, collaboration, prioritization, and change-adoption skills, with the ability to evaluate effort, dependencies, risk, business value, and technical trade-offs.
Commitment to continuous learning as AI technologies, engineering practices, security expectations, and regulatory requirements evolve.