Director, AI Platform

Scotiabank•Dallas, TX

About The Position

The Director, AI Platform, is a senior engineering leader responsible for defining, building, and scaling the enterprise AI platform that enables safe, governed, and reusable AI capabilities across the organization. This role will lead the delivery of platform services that accelerate AI adoption, improve developer productivity, and ensure AI solutions are deployed with the reliability, security, observability, and controls required in a highly regulated environment. You will partner with senior technology, data, risk, security, and business leaders to drive the AI platform roadmap, engineering execution, and enterprise adoption of core AI capabilities including model enablement, agentic AI services, orchestration, evaluation, monitoring, guardrails, prompt and context management, integration patterns, and responsible AI controls.

Requirements

Bachelor’s degree in computer science, engineering, information technology, data science, or a related technical discipline.
Experience in financial services or other highly regulated industries, with a strong understanding of security, risk, compliance, and operational control expectations.
10+ years of technology and engineering experience, including 5+ years leading platform, AI, data, cloud, or enterprise engineering teams.
Hands-on leadership experience with: AI, machine learning, generative AI, or agentic AI platforms; Cloud-native platform engineering, APIs, microservices, CI/CD, and infrastructure automation; Model deployment, orchestration, monitoring, evaluation, and operational support patterns; Strong understanding of responsible AI, AI governance, model risk, security, privacy, and regulatory expectations for production AI systems.
Experience designing platforms that support reusable AI services, developer enablement, observability, and enterprise adoption at scale.
Cloud platform expertise, with Azure preferred.
Deep expertise in platform engineering practices, AI delivery lifecycle, software engineering excellence, and operating production-grade services.
Strong understanding of: AI security, privacy, responsible AI, model lifecycle management, and regulatory compliance in a financial services environment.
Proven ability to work directly with engineers, architects, product leaders, data scientists, risk partners, and senior stakeholders.
Exceptional communication skills with the ability to translate AI platform strategy into clear engineering priorities, executive narratives, and measurable business outcomes.

Responsibilities

Define and deliver the enterprise AI platform strategy, roadmap, and engineering execution model aligned to business priorities, technology standards, and responsible AI requirements.
Build reusable platform capabilities that enable teams to develop, test, deploy, and operate AI solutions consistently and securely across the enterprise.
Establish scalable frameworks for: Model, foundation model, and large language model enablement; Agentic AI orchestration, workflow automation, and tool integration; Prompt, context, retrieval, and knowledge grounding services; Reusable APIs, SDKs, templates, and reference patterns for AI engineering teams.
Implement enterprise-grade AI platform controls including: Secure access, identity, entitlement, and policy enforcement for AI services; Responsible AI guardrails, safety patterns, evaluation gates, and human-in-the-loop controls; Auditability, traceability, model usage tracking, and evidence generation.
Lead high-performing platform engineering teams, setting clear technical direction, delivery standards, and operating rhythms.
Partner with application, data, cloud, cyber, risk, and architecture teams to embed AI platform capabilities into enterprise delivery workflows.
Ensure the AI platform supports regulated use cases by design, with controls integrated into engineering pipelines rather than applied as after-the-fact reviews.
Establish and deliver an AI operations framework that enables reliable, measurable, and governed AI services in production.
Deliver platform capabilities for: Model and agent monitoring, performance tracking, and drift detection; Evaluation, red-teaming support, quality scoring, and regression testing; Cost, token, capacity, and usage observability across AI workloads; Incident management, rollback patterns, and continuous improvement of AI services.
Embed testing, monitoring, and governance checks into AI delivery pipelines to ensure trust, resiliency, and operational readiness by design.
Create a platform experience that makes AI capabilities easy to discover, consume, and reuse across engineering and business teams.
Enable governed reuse through: AI service catalogs, reusable components, and approved reference architectures; Standard onboarding patterns, developer documentation, and self-service capabilities; Reusable evaluation datasets, prompt libraries, and implementation blueprints.
Drive adoption of AI platform capabilities by partnering with product, engineering, architecture, and business stakeholders to convert high-value AI use cases into scalable enterprise patterns.