AI Solutions Engineer

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.

The Work

The Technology Workplace Services AI Solutions Engineer identifies, designs, builds, governs, and improves AI-enabled and automated solutions that strengthen TWS service delivery, employee productivity, operational efficiency, and the digital workplace experience. The role combines business analysis and process improvement with hands-on solution engineering using approved enterprise AI, automation, analytics, scripting, and low-code platforms. The incumbent works with TWS, business stakeholders, technology teams, and control functions to deliver secure, scalable, supportable, and measurable solutions with appropriate testing, documentation, change management, monitoring, and human oversight. Targeted TWS operational support remains part of the role to maintain service context, validate outcomes, and support business continuity.

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.