Senior Manager, AI Engineering

Scotiabank•Dallas, TX

About The Position

Scotiabank is seeking a Senior Manager to lead multidisciplinary engineering teams delivering AI and agentic solutions across Canadian Banking, International Banking, Global Banking and Markets, Global Wealth Management, Fraud, and Anti-Money Laundering. The role combines people leadership, business partnership, and end-to-end delivery accountability across data engineering, AI engineering, software engineering, and MLOps. The Senior Manager will translate business priorities into executable roadmaps, develop capable teams, and oversee solutions from discovery through production adoption and ongoing operation. The role partners with Principal Engineers, architects, product owners, and platform teams to guide technical decisions, manage delivery dependencies, and ensure solutions meet enterprise expectations for security, quality, reliability, and measurable business value. It also oversees engineering delivery using technologies such as Databricks, Apache Spark, Delta Lake, Python, SQL, MLflow, and Azure services, while ensuring appropriate lifecycle controls, operational readiness, responsible AI practices, and governance.

Requirements

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.

Nice To Haves

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.

Responsibilities

Lead, develop, hire, onboard, coach, and manage the performance of engineers across data, AI, software, and MLOps disciplines; establish responsibilities, expectations, development plans, and succession plans.
Build an inclusive, collaborative, and accountable team culture; plan team capacity and skills coverage, balance priorities, and address staffing gaps.
Empower technical leads and Principal Engineers to guide architecture and engineering practices while maintaining accountability for team outcomes.
Partner with business leaders, product owners, and domain experts to identify and prioritize AI, machine learning, generative AI, and agentic workflow opportunities.
Assess business value, data readiness, technical feasibility, risk, cost, and operational impact; agree on scope, success measures, business ownership, and adoption plans.
Communicate roadmaps, progress, trade-offs, risks, and decisions to business stakeholders and senior leadership.
Own delivery roadmaps, milestones, resource plans, and execution across AI and data initiatives from proof of concept through production rollout.
Partner with product owners on prioritized backlogs, acceptance criteria, release readiness, and production handover requirements.
Manage cross-team dependencies, delivery risks, scope changes, and escalations involving 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, and use evidence to improve execution.
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 engineering 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 evaluation criteria for predictive performance, retrieval quality, response accuracy, tool execution, and task completion.
Champion code reviews, automated testing, documentation, reusable components, and disciplined software engineering practices.
Ensure 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, 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.
Embed privacy, security, auditability, explainability, and responsible AI requirements into delivery plans and release criteria.
Coordinate reviews and approvals with model risk management, compliance, data governance, and cybersecurity teams.
Ensure human oversight, sensitive data controls, and safeguards against prompt injection, unsafe tool execution, and unintended agent actions.
Maintain accountability for control implementation, evaluation evidence, audit findings, and remediation commitments.