Senior Manager, AI Engineering

Scotiabank; Dallas (TX); Presencial; USD 180.000 - 260.000; Jornada completa

Descripción de la vacante

Scotiabank is seeking a Senior Manager to lead multidisciplinary engineering teams delivering AI and agentic solutions across banking domains. You will translate business priorities into executable roadmaps, build capable teams, and oversee end-to-end delivery from discovery through production adoption and ongoing operation.

Partner with Principal Engineers, architects, product owners, and platform teams to govern design choices, ensure security and reliability, and deliver measurable business

Formación

• Experience leading AI/data engineering teams across data, AI, software, and MLOps.
• Ability to translate business priorities into executable roadmaps and deliver outcomes.
• Strong collaboration with architects, product owners, and platform teams.

Responsabilidades

• Lead multidisciplinary engineering teams across data, AI, software, and MLOps disciplines.
• Partner with line-of-business leaders, product owners, and domain experts to identify opportunities for AI and ML.
• Own delivery roadmaps, milestones, resource plans, and execution from concept to production.

Conocimientos

People leadership

Team development

Hiring & onboarding

Strategic planning

Stakeholder communication

Herramientas

Databricks

Apache Spark

Delta Lake

Python

SQL

MLflow

Azure

Descripción del empleo

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