Head, AI & Agentic Engineering

Scotiabank•Dallas, TX

21h

About The Position

The Head of AI and Agentic Engineering leads the design, delivery, and enterprise-wide adoption of the full spectrum of applied AI capabilities at Scotiabank: generative AI, agentic workflows, machine learning, and decision science. This role is responsible for translating these capabilities into production-grade solutions that create measurable business value across Scotiabank’s global business lines. Working in close partnership with business unit technology leaders, enterprise AI and data teams, and strategic industry partners, this leader ensures that AI and ML solutions are production-ready, explainable, governed, and aligned with Scotiabank’s strategic priorities.

Requirements

Bachelor’s degree in Computer Science, Computer Engineering, Software Engineering, Statistics, Mathematics, or a related quantitative discipline; Master’s degree or higher preferred.
Proven track record as a hands-on engineering leader, you have personally built and shipped production AI and software systems, and lead from the front.
Deep expertise in generative AI and agentic system design, including LLM orchestration, RAG pipelines, and multi-agent architectures, paired with solid grounding in machine learning and statistical modelling, spanning supervised and unsupervised learning, time series forecasting, causal inference, and experimental design.
Demonstrated experience managing the ML model lifecycle in regulated financial services environments, including model development, validation, champion-challenger testing, performance monitoring, and alignment with model risk governance standards consistent with OSFI E-23 and other banking regulations, with the ability to engage model risk and compliance teams as a credible technical counterpart.
Demonstrated experience delivering enterprise-scale AI solutions in regulated environments, with a strong understanding of AI governance, risk management, and compliance.
Strong ability to engage business stakeholders at all levels, translating strategic priorities into technical delivery and communicating complex AI concepts to non-technical audiences.
Experience managing strategic external partnerships with AI ecosystem partners, academic institutions, and technology vendors.
Proven success building, leading, and scaling high-performing engineering teams, with a focus on delivery velocity, quality, and developer growth.
Knowledge of cloud platforms (Azure, GCP, or AWS), modern AI/ML infrastructure patterns, and ML engineering tooling, including feature stores, model registries, experiment tracking frameworks, and batch and real-time inference pipelines.
Entrepreneurial mindset with a bias for action, comfortable with ambiguity and skilled at driving outcomes in fast-moving, high-stakes environments.
Excellent written and verbal communication skills, with a demonstrated ability to develop executive-grade communications including Board presentations, SteerCo briefings, and strategic narratives that drive alignment and investment decisions at the highest levels of the organization.
Proven ability to build and maintain relationships with senior executives, board members, and external stakeholders, representing the organization with credibility and clarity.

Responsibilities

Lead and grow a high-performing, multidisciplinary team of AI engineers, ML engineers, data scientists, full-stack developers, and solution architects to deliver production-grade AI, ML, and decision science solutions at enterprise scale.
Define and execute the enterprise AI solutions roadmap, translating business priorities across Scotiabank’s business lines into concrete AI capabilities and measurable outcomes.
Partner with business unit technology leaders across CB, IB, GTB, GBM, Wealth, and other business lines and corporate functions to identify, prioritize, and deliver high-impact AI use cases.
Design and deliver predictive and prescriptive ML and decision science solutions, spanning credit risk modeling, customer propensity and attrition prediction, fraud and financial crime detection, next-best-action optimization, and financial forecasting, ensuring production quality, model explainability, performance monitoring, and full model lifecycle governance across all business lines.
Drive enterprise-wide adoption of the full AI and ML capability stack, including generative AI, agentic workflows, ML-powered decision automation, and decision science frameworks, building education programs, best practices, and enabling frameworks for both business and technology teams.
Manage strategic industry partnerships with AI ecosystem partners, academic institutions, and technology vendors to bring leading-edge capabilities into Scotiabank.
Represent Scotiabank at industry conferences, panels, and academic forums to elevate the bank’s global AI profile and strengthen its ranking as a leading AI-enabled financial institution.
Drive intellectual property development by contributing to AI-related patents, positioning Scotiabank as an innovator and protecting proprietary AI methodologies and solutions.
Collaborate with the AI Platform and enterprise architecture teams to consume shared infrastructure, ensuring solutions align with enterprise patterns and governance frameworks without duplicating platform capabilities.
In advance of a dedicated AI Platform VP hire, take interim stewardship of the enterprise AI Platform, advancing its foundational capabilities, architecture, and adoption to ensure it is fit for purpose for enterprise-wide AI solutions delivery. Transition platform ownership cleanly and completely to the incoming VP upon their appointment.
Co-shape and contribute to the evolution of enterprise AI and model risk governance standards, including Scotiabank’s AI Risk Management Guidelines, ML model validation frameworks, and OSFI regulatory alignment, while ensuring all AI, ML, and decision science solutions are built, validated, and deployed in full compliance.
Contribute practitioner insights to the enterprise AI strategy, informing platform investment decisions, governance evolution, and executive roadmaps.
Champion secure, compliant AI development practices and foster a culture of engineering excellence across the team.
Mentor senior engineers and technical leads, building organizational capability and career growth pathways within the AI solutions practice.
Champion a high-performance, inclusive work environment that attracts and retains top AI engineering talent.