Senior AI Solutions Engineer

Scotiabank • Dallas, TX

3d

About The Position

The Senior Software Engineer, AI and Data Solutions, designs and delivers production-grade software, data, and AI-enabled solutions across the organization’s business lines. The role works across application engineering, data processing, platform integration, and AI solution delivery, translating business requirements into secure, scalable, and maintainable software. The engineer contributes to solutions integrating enterprise applications, data platforms, Customer Data Platforms, APIs, event-driven services, and AI capabilities. They provide technical leadership within delivery squads, guide less-experienced engineers, and help establish reusable engineering patterns for enterprise-scale implementation. The role independently leads technical design and implementation of complex solution components while collaborating with architecture, platform, data, security, and product teams.

The position includes building batch and real-time data-processing solutions, developing and integrating APIs and event-driven workflows, delivering Customer Data Platform and customer-data capabilities, and contributing to AI-enabled applications. The engineer applies security, privacy, observability, resilience, and responsible AI controls throughout development, supports production operations, and helps teams improve engineering practices. The location listed is Dallas, Texas.

Requirements

• 7+ years of software engineering, data engineering, platform engineering, or AI/ML solution-delivery experience.
• Demonstrated experience independently designing and delivering complex production software in cloud or enterprise environments.
• Strong programming skills in one or more languages such as Java, Python, C#, TypeScript, or Go.
• Strong experience with RESTful APIs, microservices, distributed systems, event-driven architecture, relational or NoSQL databases, automated testing, CI/CD pipelines, and cloud-native development.
• Hands-on experience building data-processing pipelines using Apache Spark, Databricks, Kafka, Google Cloud Dataflow, Azure Data Factory, or equivalent technologies.
• Working knowledge of Customer Data Platform concepts and technologies such as Adobe Experience Platform, Salesforce Data Cloud, Tealium, Twilio Segment, Microsoft Dynamics 365 Customer Insights, Treasure Data, or comparable platforms.
• Understanding of customer identity resolution, unified profiles, segmentation, consent, data lineage, data quality, activation, and customer-360 patterns.
• Experience implementing data transformations, reconciliation rules, duplicate detection, matching logic, and data-quality controls.
• Experience with at least one major cloud platform, preferably Google Cloud Platform or Microsoft Azure.
• Familiarity with AI and GenAI application patterns, including prompt orchestration, retrieval-augmented generation, vector search, tool integration, model evaluation, safety controls, and monitoring.
• Experience with containerization and orchestration technologies such as Docker and Kubernetes.
• Strong understanding of software security, privacy by design, secrets management, access control, logging, monitoring, and production support.
• Ability to communicate technical options, risks, and trade-offs to engineering teams, architects, product owners, and business stakeholders.
• Experience mentoring engineers and leading technical delivery within an Agile product or platform team.

Nice To Haves

• Bachelor’s or master’s degree in computer science, software engineering, data engineering, information systems, or a related field.
• Experience working in financial services or another highly regulated industry.
• Experience with Google Cloud Vertex AI, Azure Databricks, Apigee X, Azure API Management, LangGraph, Google ADK, or vector-search technologies.
• Cloud, data, security, or software-engineering certifications from Google Cloud, Microsoft Azure, Databricks, or equivalent providers.

Responsibilities

• Design, build, test, deploy, and operate scalable software and data solutions using modern engineering practices.
• Lead implementation of complex application components, APIs, microservices, data-processing pipelines, and event-driven integrations.
• Translate business and technical requirements into solution designs, implementation plans, technical specifications, and engineering tasks.
• Build batch and real-time data-processing solutions to ingest, validate, transform, reconcile, enrich, and distribute structured and unstructured data.
• Design and implement Customer Data Platform and customer-data capabilities, including profile creation, identity matching, profile unification, segmentation, consent and preference management, and downstream activation.
• Contribute to data-quality controls, customer identity crosswalks, matching rules, survivorship logic, lineage, and traceability across source and consuming systems.
• Develop and integrate REST APIs, asynchronous services, streaming components, and event-driven workflows.
• Contribute to AI-enabled applications, including retrieval-augmented generation, intelligent document processing, conversational experiences, workflow automation, and model-assisted decision support.
• Integrate applications with enterprise AI and data services, including Google Cloud Vertex AI, Azure Databricks, vector-search platforms, API management services, and approved large language models.
• Apply security, privacy, observability, resilience, and responsible AI controls throughout the software-development lifecycle.
• Write unit, integration, contract, performance, and automated regression tests, maintaining appropriate quality and coverage standards.
• Lead or participate in architecture, design, code, security, and production-readiness reviews.
• Diagnose complex production issues across applications, data pipelines, APIs, cloud services, and distributed systems.
• Establish reusable engineering patterns, shared libraries, coding standards, and implementation guidance for delivery teams.
• Mentor mid-level and junior engineers through design reviews, pair programming, code reviews, and technical coaching.
• Collaborate with product owners, architects, data engineers, AI/ML engineers, platform teams, cybersecurity, risk, and business-domain specialists.
• Identify delivery risks, technical debt, data dependencies, and platform limitations, and recommend practical remediation options.
• Support proof-of-concept and technical-validation activities, including acceptance criteria, evaluation methods, and production-readiness assessments.

Benefits

• Flexible benefit programs designed to support family, financial, physical, mental, and social health needs.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed