Director, AI Solutions Engineering

Scotiabank•Dallas, TX

About The Position

The Director, AI Solutions Engineering, is a senior engineering leader responsible for translating priority business opportunities into secure, scalable, production-grade AI solutions. In partnership with business lines, product teams, data teams, risk partners, and the enterprise AI platform organization, this role leads a portfolio of AI use cases from discovery and prioritization through implementation, adoption, and value realization. The Director 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 role also develops engineering talent and provides hands-on technical leadership across software, data, platform integration, MLOps, and LLMOps. It reports to a Senior Director and is accountable for an assigned solution portfolio, delivery teams, and business-line partnerships rather than overall function-wide strategy.

Requirements

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.
Proven leadership of cross-functional pods delivering measurable business outcomes 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 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 and 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.

Nice To Haves

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.
Experience delivering production AI/ML, GenAI, or data-intensive solutions in financial services or another regulated industry.

Responsibilities

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 AI opportunities, including GenAI assistants, intelligent document processing, agentic workflows, decision support, personalization, and risk-signal generation.
Translate business objectives into delivery plans that define scope, data dependencies, architecture decisions, control requirements, adoption measures, and expected outcomes.
Maintain solution blueprints and reusable implementation patterns for adoption by other business-line teams.
Lead cross-functional delivery pods involving engineering, product, business, data, platform, 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 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.
Oversee the 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.
Guide teams in building secure batch and real-time data-processing pipelines to 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 Customer Data Platform and customer-360 solutions, 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.
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.
Promote responsible experimentation while maintaining clear boundaries for sensitive data, regulated decisions, external models, tools, agents, and production access.
Partner with AI Platform Engineering and enterprise architecture to translate solution demand into platform requirements and improve shared capabilities.
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 portfolio reporting and decision recommendations to business sponsors, CIO organizations, data and AI leadership, risk partners, and executive governance forums.
Lead, coach, and develop a high-performing team of AI solutions engineers and technical leads.
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 technical standards, reusable assets, mentoring, and knowledge sharing to the broader AI engineering community of practice.