AI Engineer

Requisition ID: 278761

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 candidates relevant knowledge skills and experience.

Join a purpose driven winning team committed to results in an inclusive and high-performing culture.

Purpose:

We are seeking a Senior AI Engineer to design build and operate AI and agentic solutions supporting Canadian Banking International Banking Global Banking and Markets (GBM) Global Wealth Management (GWM) Fraud and Anti-Money Laundering (AML).
This is a hands-on engineering role spanning AI applications data pipelines software services and MLOps. You will work with business stakeholders product owners data scientists and platform teams to translate business workflows into secure reliable production solutions using Databricks MLflow Kubernetes and the Azure-native stack.
You will own the design and delivery of assigned components and workflows contribute to technical decisions with Principal Engineers and architects and support solutions throughout their production lifecycle. The role requires sound judgment in choosing between deterministic automation machine learning generative AI and agentic approaches.

What Youll Do

Business Discovery and Solution Design
Participate in discovery sessions with business stakeholders to understand workflows data sources pain points and opportunities for AI-enabled improvement.
Translate requirements into technical designs implementation tasks estimates and testable acceptance criteria in partnership with product owners and technical leads.
Assess data readiness integration dependencies and technical feasibility; build focused prototypes to validate assumptions before production implementation.
Identify appropriate boundaries for agent autonomy human review approvals and deterministic business rules.

AI and Agentic Application Engineering
Build LLM-powered applications and agentic workflows using tool calling enterprise APIs retrieval-augmented generation structured outputs and state management.
Implement document ingestion parsing chunking embeddings indexing retrieval and reranking to provide relevant permission-aware context.
Integrate predictive models and business rules with AI workflows in collaboration with data scientists and domain experts.
Implement validation timeouts retries idempotency and fallback paths to make workflows dependable and prevent unintended repeated actions.
Compare model and orchestration options against task quality latency cost and operational requirements.

Data and Software Engineering
Develop and maintain batch and streaming data pipelines using Azure Databricks Apache Spark Delta Lake Python and SQL.
Implement data quality checks schema validation lineage and access controls using enterprise standards and Unity Catalog where applicable.
Build tested APIs and services that connect AI applications to enterprise data business systems and approved tools.
Write maintainable code with clear interfaces unit and integration tests documentation and peer review; troubleshoot complex issues across application and data layers.

Evaluation MLOps and Production Delivery
Use MLflow and supporting tools to track experiments register models version prompts and configurations and retain evaluation results and deployment evidence.
Build representative evaluation datasets and automated checks for model performance retrieval relevance grounded responses tool execution and end-to-end task completion.
Implement CI/CD pipelines automated regression testing environment configuration and controlled releases with rollback support.
Containerize services with Docker and deploy to Kubernetes including Azure Kubernetes Service (AKS) using approved infrastructure-as-code patterns.
Instrument applications for tracing logs and metrics; monitor AI quality data and model drift where applicable latency availability and consumption costs.
Investigate production incidents perform root-cause analysis maintain runbooks and contribute to operational support under the teams agreed support model.

Security and responsible AI
Implement least-privilege access secure secrets management data protection and audit logging using enterprise security and Azure identity standards.
Apply safeguards for prompt injection sensitive data disclosure unsafe tool execution and unauthorized agent actions.
Implement human approval and escalation steps for sensitive decisions or actions preserving traceability of inputs outputs and tool activity.
Work with cybersecurity model risk compliance and data governance partners to address required controls and remediation items.

Collaboration and Engineering Contribution
Work with Principal Engineers and architects to align component designs with enterprise architecture and reusable platform capabilities.
Collaborate with data engineers data scientists platform engineers and business teams to resolve dependencies and deliver integrated solutions.
Mentor engineers through code reviews pairing and knowledge sharing; contribute reusable components and practical engineering guidance.
Communicate progress blockers technical trade-offs and delivery risks clearly and early.

Required Qualifications
Strong professional experience in software data or machine learning engineering including hands-on delivery of production AI or machine learning solutions.
Proficiency in Python and SQL API development automated testing Git and modern software engineering practices.
Hands-on experience building generative AI applications or agentic workflows including retrieval tool integration orchestration and evaluation.
Experience with Databricks Apache Spark Delta Lake and scalable data processing pipelines.
Experience with MLflow and MLOps practices such as experiment tracking model registration versioning deployment and monitoring.
Practical experience with Microsoft Azure services and cloud application security identity networking and observability.
Experience with Docker Kubernetes CI/CD pipelines and infrastructure as code with the ability to deploy and troubleshoot services.
Ability to independently deliver well-defined engineering work investigate ambiguous problems and collaborate across business and technical teams.
Understanding of AI evaluation data privacy secure tool use human oversight and the limitations of probabilistic model outputs.

What Youll Bring

Required Qualifications
Strong professional experience in software data or machine learning engineering including hands-on delivery of production AI or machine learning solutions.
Proficiency in Python and SQL API development automated testing Git and modern software engineering practices.
Hands-on experience building generative AI applications or agentic workflows including retrieval tool integration orchestration and evaluation.
Experience with Databricks Apache Spark Delta Lake and scalable data processing pipelines.
Experience with MLflow and MLOps practices such as experiment tracking model registration versioning deployment and monitoring.
Practical experience with Microsoft Azure services and cloud application security identity networking and observability.
Experience with Docker Kubernetes CI/CD pipelines and infrastructure as code with the ability to deploy and troubleshoot services.
Ability to independently deliver well-defined engineering work investigate ambiguous problems and collaborate across business and technical teams.
Understanding of AI evaluation data privacy secure tool use human oversight and the limitations of probabilistic model outputs.

Preferred Qualifications
Experience in financial services particularly banking capital markets wealth management fraud detection or AML.
Experience implementing retrieval and vector search document processing semantic models or knowledge graph integrations.
Familiarity with Model Context Protocol (MCP) event-driven architectures and agent orchestration frameworks.
Experience working in regulated environments with model risk audit and data governance requirements.
Bachelors or masters degree in computer science engineering mathematics or a related discipline or equivalent practical experience.

Technology Environment
Data engineering: Azure Databricks Apache Spark Delta Lake Unity Catalog Azure Data Lake Storage Azure Data Factory and Azure Event Hubs.
AI and machine learning: Azure-hosted AI models and services MLflow machine learning frameworks agent orchestration frameworks retrieval and vector search.
Software and cloud: Python SQL REST APIs Microsoft Azure Docker Kubernetes AKS Azure Functions and Azure API Management.
Engineering operations: Azure DevOps or GitHub Actions Terraform or Bicep Azure Monitor Application Insights Key Vault and Microsoft Entra ID.

Hiring Manager: Prabhu Gururaj

Interested

If your experience is closely related but doesnt 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. Thats why we work to grow and diversify talent and engage employees in a performance-oriented culture.

Whats 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.

#Dallas

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.

Required Experience:

IC