AI Enablement Engineer

AI Enablement Engineer

Join a purpose driven winning team, committed to results, in an inclusive and high-performing culture. Global Banking and Markets (GBM) is a leading Canadian Capital Markets and Investment Banking business with a growing platform in the US and Latin America, operating globally for over 100 years. Scotiabank's strong U.S. presence provides our clients an important bridge to this key global market for trade and investment flows across the Americas and the world. Global Banking & Markets provides a full range of investment banking, credit and risk management products and services relevant to the financing and strategic development needs of our clients.

Be part of an innovative, Global Capital Markets and Investment Banking business with a unique geographic footprint that puts capital to work for our clients across industries! We work together to drive ambition for every future!

Purpose: As an AI Enablement Engineer, you will act as the technical enablement and functional interface between enterprise Artificial Intelligence platforms and the organization. You will be responsible for enabling scalable adoption, effective usage, and measurable value realization of generative AI, LLM-powered services, and intelligent productivity platforms across the enterprise. This role is highly technical in orientation but not development focused. Your mandate is to ensure that AI platforms are understood, operationalized, and embedded into business workflows through structured enablement, technical documentation, and functional guidance. You will translate platform capabilities into practical usage patterns, ensuring teams can safely and effectively leverage AI tools as they mature.

Platform Enablement & User Onboarding: Design and execute technical onboarding frameworks for enterprise AI platforms, including capability overviews, access models, guardrails, and supported use patterns. Produce technical enablement artifacts such as system usage guides, configuration references, operating manuals, and structured platform documentation. Partner with technology, architecture, data, and business teams to integrate AI capabilities into existing workflows and operating models. Analyze end-to-end workflows and identify opportunities where AI-driven automation or augmentation can improve efficiency and consistency. Lead structured discovery and intake sessions to capture technical and business requirements for AI-enabled workflows.

Capability Roadmap & Platform Strategy: Track and assess the evolving capabilities of enterprise AI platforms, including generative AI services, copilots, and intelligent productivity tools. Translate vendor and platform roadmaps into internal capability roadmaps, clearly outlining what is available, what is coming, and how it should be used. Evaluate new features for technical readiness, risk alignment, and business applicability, and communicate implications to stakeholders. Act as a functional advisor to platform and engineering teams during feature rollouts and upgrades.

Technical Training & Knowledge Enablement: Build and maintain best-practice repositories, including prompt engineering patterns, usage standards, workflow templates, and reference examples. Establish and manage a distributed AI Champions network to support advanced usage, experimentation, and peer enablement. Support platform delivery teams by ensuring users are technically prepared to adopt new capabilities through guided usage and structured learning paths. Enable smooth transition from platform implementation to steady-state usage through targeted enablement and adoption support.

Functional Support & Continuous Improvement: Serve as the primary functional and enablement point of contact for AI platform usage questions, workflow troubleshooting, and best-practice guidance. Triage functional issues, usability gaps, and platform defects, escalating appropriately to engineering, architecture, or IT teams. Collect, synthesize, and prioritize user feedback to inform future platform enhancements, roadmap decisions, and enablement strategies. Perform functional validation and user acceptance testing to ensure AI solutions meet business and usability requirements. Continuously refine enablement materials and workflows based on real-world usage and post-rollout feedback.

Value Realization, Adoption & Metrics: Define and monitor AI adoption and usage metrics, including activation, engagement, satisfaction, and feature utilization. Measure productivity impact, efficiency gains, and value realization through surveys, interviews, and usage data. Document and communicate internal success stories, proven patterns, and repeatable use cases to drive broader adoption. Produce enablement and adoption reporting that demonstrates business impact and informs leadership decision-making.

What You'll Bring: Demonstrated experience in technical enablement, platform adoption, systems analysis, or digital transformation roles. Experience working in Azure, Databricks and Google native services and environments Strong conceptual understanding of Generative AI, Large Language Models (LLMs), AI copilots, and enterprise productivity platforms. Ability to interpret complex platform capabilities and translate them into clear operational guidance and usage standards. Excellent written and verbal communication skills, particularly for technical documentation and stakeholder presentations. Strong user-centric mindset with the ability to anticipate adoption challenges and design practical solutions. Experience defining metrics, analyzing adoption data, and producing insights reports. 5-7 years of experience in a role such as Technical Systems Analyst, Platform Enablement Lead, or Technical Product Owner Bachelor's degree in, Information Sciences, Computer Information Systems, or a related field. Spanish fluency (nice to have).