Senior Manager, AI Platform Engineering

Scotiabank Group
Dallas, TX, United States
6 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
3 years minimum
Working hours
Regular working hours
Job source

Tech stack

Application Programming Interfaces (APIs) Artificial Intelligence Audit Trail Microsoft Azure Cloud Computing Cloud Foundry Continuous Integration Machine Learning Regression Testing Software Engineering Cloud Platform System Large Language Models
+9 more
Model Validation Generative AI Usage Tracking AI Platforms Infrastructure Automation Frameworks Information Technology Machine Learning Operations Virtual Agents Microservices

Job description

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.

What You’ll Do

AI Platform Strategy & Engineering Execution: 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: o Model, foundation model, and large language model enablement o Agentic AI orchestration, workflow automation, and tool integration o Prompt, context, retrieval, and knowledge grounding services o Reusable APIs, SDKs, templates, and reference patterns for AI engineering teams Implement enterprise-grade AI platform controls including: o Secure access, identity, entitlement, and policy enforcement for AI services o Responsible AI guardrails, safety patterns, evaluation gates, and human-in-the-loop controls o 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.

AI Operations, Observability & Trust: Implement and operate an AI operations framework that enables reliable, measurable, and governed AI services in production. Deliver platform capabilities for: o Model and agent monitoring, performance tracking, and drift detection o Evaluation, red-teaming support, quality scoring, and regression testing o Cost, token, capacity, and usage observability across AI workloads o 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.

AI Enablement, Reuse & Adoption: Create a platform experience that makes AI capabilities easy to discover, consume, and reuse across engineering and business teams. Enable governed reuse through: o AI service catalogs, reusable components, and approved reference architectures o Standard onboarding patterns, developer documentation, and self-service capabilities o 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.

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: o AI, machine learning, generative AI, or agentic AI platforms o Cloud-native platform engineering, APIs, microservices, CI/CD, and infrastructure automation o Model deployment, orchestration, monitoring, evaluation, and operational support patterns o Strong understanding of responsible AI, AI governance, model risk, security, privacy, and regulatory expectations for production AI systems. o Experience designing platforms that support reusable AI services, developer enablement, observability, and enterprise adoption at scale. o 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: o 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.

About the company

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. Our products include debt and equity financing, mergers & acquisitions, corporate banking, institutional equity sales, trading and research, fixed income products, derivatives, energy, foreign exchange and precious & metals. We also cross-sell the full range of wholesale products and services offered by the Scotiabank Group. 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!, Scotiabank is a leading bank in the Americas. Guided by our purpose: “for every future”, we help our customers, their families and their communities achieve success through a broad range of advice, products and services, including personal and commercial banking, wealth management and private banking, corporate and investment banking, and capital markets.

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