Director, AI Solutions Engineering

Requisition ID: 279038

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

The Director, AI Solutions Engineering, is a senior engineering leader responsible for translating priority business opportunities into secure, scalable, production-grade AI solutions. Working in partnership with business lines, product teams, data teams, risk partners, and the enterprise AI platform organization, the Director leads a portfolio of AI use cases from discovery and prioritization through implementation, adoption, and value realization.

The role leads cross-functional solution pods, establishes reusable delivery patterns, and ensures predictive AI, generative AI, and agentic solutions are measurable, responsible, reliable, and aligned with enterprise architecture and governance standards. The Director also develops engineering talent and provides hands-on technical leadership across software, data, platform integration, MLOps, and LLMOps.

This role reports to the Senior Director level, with accountability for an assigned solution portfolio, delivery teams, and business-line partnerships rather than the overall function-wide strategy.

What You'll Do

AI Solution Portfolio and Roadmap

• Develop and manage the roadmap for an assigned portfolio of AI solutions and priority business use cases, aligned with enterprise AI strategy, intake, funding, governance, and platform direction.
• Partner with business and technology leaders to identify, assess, and prioritize high-value opportunities such as GenAI assistants, intelligent document processing, agentic workflows, decision support, personalization, and risk-signal generation.
• Translate business objectives into practical delivery plans with clear scope, data dependencies, architectural decisions, control requirements, adoption measures, and expected business outcomes.
• Maintain solution blueprints and reusable implementation patterns that allow other business-line teams to replicate proven approaches.

Hub-and-Spoke Delivery Leadership

• Lead cross-functional delivery pods comprising AI and software engineers, product owners, business SMEs, data specialists, platform teams, cybersecurity, architecture, risk, privacy, and compliance partners.
• Own delivery outcomes for assigned solutions, including technical quality, schedule, risk management, production readiness, adoption, operational performance, and measurable value.
• Provide hands-on engineering enablement through reference implementations, architecture guidance, reusable CI/CD modules, evaluation harnesses, coding standards, and design reviews.
• Create transparent delivery governance using milestones, dependency tracking, risk registers, service-level objectives, and executive-ready status reporting.

AI Engineering, MLOps and LLMOps

• Oversee design and implementation of predictive AI, GenAI, retrieval-augmented generation, and agentic solutions across cloud-native application and data platforms.
• Ensure MLOps and LLMOps practices cover evaluation, versioning, deployment, monitoring, rollback, drift detection, prompt management, model and tool changes, and production incident response.
• Maintain reusable patterns for APIs, microservices, prompts, embeddings, vector stores, tool integration, runtime orchestration, observability, resilience, and cost management.
• Integrate AI engineering into the software-development lifecycle and developer toolchain, including source repositories, automated testing, CI/CD, security scanning, and engineering productivity tooling.

Data Processing and Customer Data Solutions

• Guide solution teams in building secure batch and real-time data-processing pipelines that ingest, validate, transform, reconcile, enrich, and distribute structured and unstructured data.
• Ensure AI solutions apply appropriate data-quality, lineage, metadata, retention, access-control, privacy, and observability standards.
• Provide direction for solutions that use Customer Data Platforms and customer-360 capabilities, including identity resolution, unified customer profiles, segmentation, consent and preference management, personalization, and downstream activation.
• Partner with enterprise data and customer-data teams to define integration patterns between operational systems, governed data products, Customer Data Platforms, analytics environments, and AI applications.

Responsible AI, Security and Risk

• Embed privacy, data classification, model governance, explainability, evaluation, human oversight, safety, security, and monitoring controls into solution design and delivery.
• Ensure solutions follow enterprise AI intake, architecture, cybersecurity, model-risk, third-party risk, privacy, and production-governance processes.
• Operationalize risk and control requirements through delivery checklists, automated evidence, approval gates, testing standards, and ongoing monitoring rather than documentation-only compliance.
• Promote responsible experimentation while maintaining clear boundaries for sensitive data, regulated decisions, external models, tools, agents, and production access.

Platform Partnership, Reuse and Value Realization

• Partner with AI Platform Engineering and enterprise architecture to translate solution demand into platform requirements and improve shared capabilities such as evaluation, guardrails, model routing, feature and embedding stores, vector search, and agent frameworks.
• Drive adoption of enterprise platform services, standard APIs, reusable components, and golden paths to reduce duplication, delivery time, operational risk, and run costs.
• Define and track KPIs and OKRs for assigned solutions, including cycle time, adoption, quality, reliability, risk posture, productivity, customer or employee outcomes, and return on investment.
• Provide concise portfolio reporting and decision recommendations to business sponsors, CIO organizations, data and AI leadership, risk partners, and executive governance forums.

People Leadership

• Lead, coach, and develop a high-performing team of AI solutions engineers and technical leads, setting clear expectations for delivery, engineering excellence, responsible AI, and business partnership.
• Build workforce and capability plans for the assigned portfolio, including hiring, succession planning, skill development, performance enablement, and deployment of specialists across solution pods.
• Foster an inclusive, accountable engineering culture that encourages constructive challenge, continuous learning, reuse, operational ownership, and evidence-based decision-making.
• Contribute to the broader AI engineering community of practice through technical standards, reusable assets, mentoring, and knowledge sharing.

What You'll Bring

• 13+ years of experience in software engineering, data engineering, AI/ML engineering, solution architecture, or technology delivery, including at least 5 years leading engineering teams or complex cross-functional delivery.
• Demonstrated success delivering production AI/ML, GenAI, or data-intensive solutions at enterprise scale, preferably in financial services or another regulated industry.
• Proven leadership of cross-functional pods delivering measurable business outcomes in partnership with business-line product owners, domain experts, data teams, and technology stakeholders.
• Strong knowledge of GenAI and agentic-system patterns, retrieval and embeddings, vector databases, prompt and policy engineering, model evaluation, guardrails, and MLOps/LLMOps.
• Strong command of cloud-native engineering on Google Cloud Platform, Microsoft Azure, or comparable platforms, including APIs, microservices, event-driven systems, containers, CI/CD, observability, and reliability engineering.
• Working knowledge of modern data processing using technologies such as SQL, Python, Apache Spark, Databricks, Kafka, Dataflow, or equivalent batch and streaming services.
• Working knowledge of Customer Data Platform and customer-360 concepts, including profile unification, identity resolution, segmentation, consent, personalization, activation, customer data quality, and lineage.
• Experience establishing reusable solution patterns, reference architectures, engineering standards, and production-readiness practices that improve delivery speed and reliability across multiple teams.
• Demonstrated ability to implement responsible AI, cybersecurity, privacy, data-governance, and model-risk requirements within engineering delivery.
• Strong stakeholder leadership with the ability to influence senior business, technology, data, risk, and compliance partners and align teams around shared outcomes.
• Experience defining and communicating solution value through KPIs, OKRs, adoption measures, reliability indicators, risk metrics, and financial or operational benefits.
• Proven ability to attract, coach, develop, and retain engineering talent while building an inclusive and high-accountability team culture.

Preferred Qualifications

• Bachelor’s or master’s degree in computer science, software engineering, data science, information systems, or a related field, or equivalent practical experience.
• Experience delivering AI or customer-data solutions within banking, insurance, capital markets, healthcare, telecommunications, or another highly regulated environment.
• Familiarity with Google Cloud Vertex AI, Azure Databricks, Apigee X, Azure API Management, LangGraph, Google ADK, MLflow, Kubernetes, and enterprise vector-search technologies.
• Exposure to Customer Data Platforms such as Adobe Experience Platform, Salesforce Data Cloud, Tealium, Twilio Segment, Microsoft Dynamics 365 Customer Insights, Treasure Data, or equivalent platforms.
• Knowledge of applicable Canadian regulatory and privacy expectations, including OSFI B-13, OSFI E-23, OSFI B-10, PIPEDA, and Quebec Law 25.
• Relevant cloud, architecture, data, security, or AI certifications from Google Cloud, Microsoft Azure, Databricks, or comparable providers.

Leadership Attributes

• Enterprise thinking: connects business priorities to reusable solution patterns, data capabilities, platform services, and enterprise standards.
• Engineering excellence: establishes practical golden paths that improve quality, security, reliability, and time-to-production.
• Risk stewardship: embeds fit-for-purpose controls into engineering workflows without creating unnecessary delivery friction.
• Talent leadership: develops strong technical leaders and creates an environment of accountability, inclusion, and continuous improvement.
• Results orientation: ships production solutions, measures adoption and value, and uses operational evidence to continuously improve.

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.

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.