{"id":3894921,"url":"https://alion.io/job/scotiabank-principal-ai-engineer","title":"Principal AI Engineer","company":{"id":1754303,"name":"Scotiabank","domain":"scotiabank.com","url":"https://alion.io/company/scotiabank-com","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SuccessFactors","truth_index":{"grade":"A","score":100,"open_postings":39,"ghost_share":0,"stale_share":0,"repost_share":0.026,"time_to_fill_p50_days":17,"computed_at":"2026-11-08T05:47:12Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Dallas, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Azure","optional":false},{"name":"Azure AKS","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Docker","optional":false},{"name":"Function Calling","optional":false},{"name":"Kubernetes","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Azure Data Factory","optional":true},{"name":"Azure DevOps","optional":true},{"name":"Bicep","optional":true},{"name":"GitHub Actions","optional":true},{"name":"Knowledge Graph","optional":true},{"name":"Microsoft Entra ID","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"Rest API","optional":true},{"name":"Terraform","optional":true}],"status":"live","first_seen_at":"2026-11-06T22:49:19Z","employer_posted_date":"2026-11-06","last_verified_at":"2026-11-08T17:00:37Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"Requisition ID: 278902\nSalary Range: -\nPlease 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.\nJoin a purpose driven winning team, committed to results, in an inclusive and high-performing culture.\nPurpose\nWe are seeking a Principal Engineer to lead the discovery, architecture, and delivery of AI and agentic solutions across Canadian Banking, International Banking, Global Banking and Markets (GBM), Global Wealth Management (GWM), Fraud, and Anti-Money Laundering (AML).\nThis is a hands-on technical leadership role spanning data engineering, AI engineering, software engineering, and MLOps. You will partner with business leaders to identify opportunities, translate complex workflows into practical solutions, and guide multidisciplinary teams from early experimentation through production deployment and ongoing operation.\nThe role requires deep engineering judgment: determining when to use traditional automation, machine learning, generative AI, or agentic workflows, and delivering solutions that meet enterprise expectations for security, reliability, explainability, and measurable business value.\nWhat You'll Do\nBusiness partnership and use case discovery\nPartner with line-of-business stakeholders, product owners, and domain experts to understand business processes, operational challenges, and opportunities for AI-driven improvement.\nLead discovery workshops to identify and prioritize AI and agentic use cases based on business value, technical feasibility, data readiness, risk, and delivery effort.\nTranslate opportunities into solution designs, delivery roadmaps, technical backlogs, and measurable acceptance criteria.\nAssess where autonomous execution is appropriate and where deterministic controls, human review, or approval are required.\nArchitecture and technical leadership\nOwn end-to-end technical architecture across data ingestion, processing, model development, retrieval, agent orchestration, application integration, and production operations.\nDesign agentic workflows incorporating tool use, enterprise APIs, retrieval-augmented generation, state management, and human oversight.\nEstablish reusable engineering patterns, reference architectures, and shared components that accelerate delivery across business lines.\nLead architecture and code reviews, resolve complex engineering challenges, and guide teams through technical trade-offs.\nBuild critical components and reference implementations while mentoring engineers and influencing technical decisions across teams.\nData and AI Engineering\nDesign scalable batch and streaming data pipelines using Databricks, Apache Spark, Delta Lake, SQL, Python, and Azure-native services.\nEstablish data quality, lineage, metadata, access controls, and curated data products that support analytics, machine learning, and AI applications.\nDevelop and integrate machine learning models, LLM-powered applications, retrieval services, and agentic workflows into enterprise systems.\nDefine evaluation approaches for predictive performance, retrieval quality, response accuracy, tool execution, and end-to-end task completion.\nMLOps DevOps and Production Delivery\nEstablish lifecycle practices for experiments, models, prompts, agents, and evaluation datasets using MLflow and supporting engineering tools.\nImplement automated testing, CI/CD, infrastructure as code, deployment controls, rollback strategies, and environment management.\nDesign and deploy containerized services using Docker and Kubernetes, including Azure Kubernetes Service (AKS).\nDefine production monitoring for data and model drift, AI quality, latency, availability, cost, and operational failures.\nPartner with platform engineering, cybersecurity, and operations teams to deliver resilient services with clear ownership, support models, and service-level objectives.\nResponsible AI and Enterprise Governance\nEmbed privacy, security, auditability, explainability, and responsible AI requirements into solution design and delivery.\nPartner with model risk management, compliance, data governance, and cybersecurity teams throughout the delivery lifecycle.\nImplement appropriate controls for sensitive data access, prompt injection, unsafe tool execution, and unintended agent actions.\nEnsure production solutions maintain traceable decisions, evaluation evidence, and appropriate human accountability.\nWhat You'll Bring\nExtensive experience in software, data, or machine learning engineering, with demonstrated technical leadership across complex enterprise initiatives.\nProven ability to take AI or machine learning solutions from business discovery through production deployment and operation.\nStrong hands-on proficiency in Python, SQL, API design, distributed systems, and modern software engineering practices.\nDeep experience with Databricks, Apache Spark, Delta Lake, and enterprise data pipeline architecture.\nPractical experience building generative AI and agentic applications, including retrieval, tool integration, orchestration, and evaluation.\nExperience with MLflow and MLOps practices, including experiment tracking, model registration, deployment, monitoring, and lifecycle governance.\nStrong knowledge of Azure architecture, identity, networking, security, and cloud-native application delivery.\nExperience with Docker, Kubernetes, CI/CD pipelines, automated testing, and infrastructure as code.\nAbility to communicate technical trade-offs clearly to executives, business stakeholders, architects, and engineering teams.\nDemonstrated ability to lead through influence and raise engineering standards across organizational boundaries.\nPreferred Qualifications\nExperience in banking or financial services, particularly banking operations, capital markets, wealth management, fraud detection, or AML.\nExperience delivering AI solutions within regulated environments and collaborating with model risk and governance functions.\nFamiliarity with Unity Catalog, enterprise metadata platforms, semantic models, vector search, and knowledge graphs.\nExperience with event-driven architectures, secure agent tool interfaces, and Model Context Protocol (MCP).\nBachelor’s or master’s degree in computer science, engineering, mathematics, or a related discipline, or equivalent practical experience.\nTechnology Environment\nData engineering: Azure Databricks, Apache Spark, Delta Lake, Unity Catalog, Azure Data Lake Storage, Azure Data Factory, Azure Event Hubs.\nAI and machine learning: Azure-hosted AI models and services, MLflow, machine learning frameworks, agent orchestration frameworks, retrieval and vector search.\nApplication engineering: Python, SQL, REST APIs, microservices, and event-driven integration.\nCloud and runtime: Microsoft Azure, Docker, Kubernetes, AKS, Azure Functions, and Azure API Management.\nEngineering operations: Azure DevOps or GitHub Actions, Terraform or Bicep, Azure Monitor, Application Insights, Key Vault, and Microsoft Entra ID.\nInterested?\nIf 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!\nAt 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.\nWhat's in it for you?\nScotiabank 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.\nLocation(s): United States : Texas : Dallas\nScotiabank 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.\nAt 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.\nScotiabank is an equal opportunity employer. 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