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Scotiabank

Senior Manager, AI Engineering

Dallas, TX, United States

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Job details

Apply by: Nov 21, 2026
Posting language: English
Posted: Nov 6, 2026

Original job description

Requisition ID: 278899

Salary Range: -

Please note that the Salary Range shown is a guideline only. Salary offered may vary based on factors, including, but not limited to, the successful candidate’s relevant knowledge, skills, and experience.

Join a purpose driven winning team, committed to results, in an inclusive and high-performing culture.

Purpose

We are seeking a Senior Manager to lead multidisciplinary engineering teams delivering AI and agentic solutions across Canadian Banking, International Banking, Global Banking and Markets (GBM), Global Wealth Management (GWM), Fraud, and Anti-Money Laundering (AML).

This management role combines people leadership, business partnership, and end-to-end delivery accountability across data engineering, AI engineering, software engineering, and MLOps. You will translate business priorities into executable roadmaps, build capable teams, and oversee solutions from discovery through production adoption and ongoing operation.

You will partner with Principal Engineers, architects, product owners, and platform teams to make sound technical decisions, manage delivery dependencies, and ensure solutions meet enterprise expectations for security, quality, reliability, and measurable business value.

What You'll Do

People leadership and team development

Lead and develop engineers across data, AI, software, and MLOps disciplines, establishing clear responsibilities, performance expectations, and individual development plans.
Own hiring, onboarding, coaching, performance management, and succession planning; build a collaborative, accountable, and inclusive team culture.
Plan team capacity and skills coverage, balance priorities across initiatives, and address staffing gaps through recruitment, development, and partner support.
Empower technical leads and Principal Engineers to guide architecture and engineering practices while maintaining clear accountability for team outcomes. Business partnership and use case discovery
Partner with line-of-business leaders, product owners, and domain experts to identify opportunities for AI, machine learning, generative AI, and agentic workflows.
Lead discovery and prioritization based on business value, data readiness, technical feasibility, risk, cost, and operational impact.
Agree on scope, success measures, business ownership, and adoption plans; ensure teams select an appropriate mix of automation, predictive models, and AI agents.
Communicate roadmaps, progress, trade-offs, risks, and decisions clearly to business stakeholders and senior leadership.

Delivery planning and execution

Own delivery roadmaps, milestones, resource plans, and execution across multiple AI and data initiatives, from proof of concept through production rollout.
Partner with product owners to maintain prioritized backlogs, define acceptance criteria, and establish release readiness and production handover requirements.
Manage cross-team dependencies, delivery risks, scope changes, and escalations across business, platform engineering, cybersecurity, governance, and operations.
Manage assigned budgets, cloud consumption, vendor commitments, and delivery costs; report forecasts and make resource trade-offs within delegated authority.
Track business outcomes, adoption, delivery predictability, engineering quality, and operational performance; use evidence to improve execution. Engineering oversight and technical direction
Provide technical management oversight across data pipelines, machine learning models, retrieval services, agent orchestration, APIs, and enterprise application integration.
Partner with Principal Engineers and architects to review solution designs and ensure alignment with enterprise architecture and reusable platform capabilities.
Oversee delivery using Databricks, Apache Spark, Delta Lake, Python, SQL, MLflow, and Azure-native services, with appropriate data quality, lineage, and access controls.
Ensure AI and agentic solutions have defined evaluation criteria covering predictive performance, retrieval quality, response accuracy, tool execution, and task completion.
Champion code reviews, automated testing, documentation, reusable components, and disciplined software engineering practices across teams. MLOps DevOps and operational accountability
Ensure teams implement lifecycle controls for data, models, prompts, agents, and evaluation datasets, including versioning, deployment approvals, and rollback.
Oversee CI/CD, infrastructure as code, containerization, and Kubernetes delivery, including Azure Kubernetes Service (AKS), in partnership with platform teams.
Establish service ownership, support readiness, observability, incident response, and agreed service-level objectives for production solutions.
Monitor model and data drift, AI quality, availability, latency, and cost; prioritize remediation and operational improvements. Responsible AI and governance
Embed privacy, security, auditability, explainability, and responsible AI requirements into delivery plans and release criteria.
Coordinate reviews and required approvals with model risk management, compliance, data governance, and cybersecurity teams.
Ensure appropriate human oversight, sensitive data controls, and safeguards against prompt injection, unsafe tool execution, and unintended agent actions.
Maintain clear accountability for control implementation, evaluation evidence, audit findings, and remediation commitments.

Required Qualifications

Extensive experience in software, data, or AI engineering, including demonstrated people management and leadership of multidisciplinary engineering teams.
Proven ability to deliver enterprise AI, machine learning, or data solutions from business discovery through production deployment and ongoing operation.
Experience hiring, coaching, developing, and managing the performance of engineers and technical leads.
Strong technical fluency in data engineering, distributed systems, APIs, and software development, with the ability to assess architecture and implementation trade-offs.
Working knowledge of Databricks, Apache Spark, Delta Lake, Python, SQL, and enterprise data pipelines.
Practical understanding of generative AI, agentic workflows, retrieval-augmented generation, tool integration, evaluation, and human oversight.
Experience overseeing MLOps and MLflow-based lifecycle practices, including experiment tracking, model registration, deployment, and monitoring.
Experience managing delivery on Microsoft Azure, including cloud security, identity, networking, Docker, Kubernetes, CI/CD, and infrastructure as code.
Demonstrated ownership of roadmaps, capacity planning, delivery risks, budgets or cost forecasts, and cross-team dependencies.
Strong stakeholder management and executive communication skills, with the ability to align business priorities and engineering execution.

What You'll Bring

Experience in banking or financial services, particularly banking operations, capital markets, wealth management, fraud detection, or AML.
Experience managing delivery in regulated environments and working with model risk management, compliance, and enterprise governance functions.
Familiarity with Unity Catalog, enterprise metadata, vector search, semantic models, knowledge graphs, and Model Context Protocol (MCP).
Experience managing geographically distributed teams, delivery partners, and vendors.
Bachelor’s or master’s degree in computer science, engineering, or a related discipline, or equivalent practical experience. Technology Environment
Data engineering: Azure Databricks, Apache Spark, Delta Lake, Unity Catalog, Azure Data Lake Storage, Azure Data Factory, and Azure Event Hubs.
AI and machine learning: Azure-hosted AI models and services, MLflow, agent orchestration frameworks, retrieval, and vector search.
Software and cloud: Python, SQL, REST APIs, microservices, Microsoft Azure, Docker, Kubernetes, AKS, Azure Functions, and Azure API Management.
Engineering operations: Azure DevOps or GitHub Actions, Terraform or Bicep, Azure Monitor, Application Insights, Key Vault, and Microsoft Entra ID.

Interested?

If your experience is closely related but doesn’t align perfectly with every qualification, we do encourage you to apply - you might be the right candidate for this or other roles at Scotiabank! At Scotiabank, every employee is empowered to reach their fullest potential, respected for who they are and, embraced for their differences. That’s why we work to grow and diversify talent and engage employees in a performance-oriented culture.

What's in it for you?

Scotiabank wants you to be able to bring your best self to work – and life, every day. With a focus on holistic well-being, our many flexible benefit programs are designed to help support your unique family, financial, physical, mental, and social health needs.

#Dallas

Location(s): United States : Texas : Dallas

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.

At Scotiabank, we value the unique skills and experiences each individual brings to the Bank, and are committed to creating and maintaining an inclusive and accessible environment for everyone. If you require accommodation (including, but not limited to, an accessible interview site, alternate format documents, ASL Interpreter, or Assistive Technology) during the recruitment and selection process, please let our Recruitment team know. If you require technical assistance, please click here. Candidates must apply directly online to be considered for this role. We thank all applicants for their interest in a career at Scotiabank; however, only those candidates who are selected for an interview will be contacted.

Scotiabank is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other characteristic protected by federal, state, or local law.

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More about this job

Skills

Anti-money laundering
Data engineering
Leadership
Team building
Recruitment
Machine learning
Prioritization
Cybersecurity
Databricks
Apache Spark
Python
SQL
Mlflow
Microsoft Azure
Test automation
Documentation
CI/CD
Infrastructure as code
Kubernetes
Incident response
Risk management
Regulatory compliance
Team leadership
Coaching
Software development
Docker
Stakeholder management
Communication skills
Fraud detection
Unity
Computer science
Microsoft Azure Data Factory
API development
Microservices
DevOps
GitHub
IBM Terraform

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