Senior Manager, AI Platform Engineering

Scotiabank•New York, NY

23h•Onsite

About The Position

The Senior Manager, AI Platform, is an experienced engineering leader responsible for managing the delivery, adoption, and continuous improvement of enterprise AI platform capabilities that enable safe, governed, and reusable AI solutions across the organization. This role will lead platform execution for 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 technology, data, risk, security, architecture, product, and business stakeholders to translate the AI platform roadmap into delivery plans, engineering priorities, and reusable platform 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.
8+ years of technology and engineering experience, including 3+ years managing or leading platform, AI, data, cloud, or enterprise engineering teams.
Hands-on leadership experience with AI, machine learning, generative AI, or agentic AI platforms.
Hands-on leadership experience with Cloud-native platform engineering, APIs, microservices, CI/CD, and infrastructure automation.
Hands-on leadership experience with 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.
Strong 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 to deliver platform outcomes.
Strong communication skills with the ability to translate AI platform strategy into clear engineering priorities, delivery plans, stakeholder updates, and measurable outcomes.

Responsibilities

Contribute to and execute the enterprise AI platform roadmap, delivery plan, and engineering priorities aligned to business needs, 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.
Establish scalable frameworks for Agentic AI orchestration, workflow automation, and tool integration.
Establish scalable frameworks for Prompt, context, retrieval, and knowledge grounding services.
Establish scalable frameworks for 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.
Implement enterprise-grade AI platform controls including Responsible AI guardrails, safety patterns, evaluation gates, and human-in-the-loop controls.
Implement enterprise-grade AI platform controls including Auditability, traceability, model usage tracking, and evidence generation.
Manage and coach platform engineering teams, setting clear delivery expectations, technical standards, sprint priorities, and operating rhythms.
Partner with application, data, cloud, cyber, risk, and architecture teams to implement AI platform capabilities within 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.
Implement and operate 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.
Deliver platform capabilities for Evaluation, red-teaming support, quality scoring, and regression testing.
Deliver platform capabilities for Cost, token, capacity, and usage observability across AI workloads.
Deliver platform capabilities for 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.
Enable governed reuse through Standard onboarding patterns, developer documentation, and self-service capabilities.
Enable governed reuse through Reusable evaluation datasets, prompt libraries, and implementation blueprints.
Drive adoption of AI platform capabilities by working with product, engineering, architecture, and business stakeholders to turn high-value AI use cases into reusable implementation patterns.