Principal AI Engineer - Tangerine

Scotiabank•Toronto, ON

22h•Onsite

About The Position

Tangerine is Canada’s leading direct bank, offering flexible and accessible banking options, innovative products, and award-winning Client service. The reason why Tangerine employees come to work each day is to help Canadians live better lives. We focus on making a difference in our communities, and that includes our own internal community. It’s important to us that our employees feel empowered and enthusiastic about belonging to our Orange culture.

Requirements

Proven experience in AI leadership and delivery roles, with a track record of successfully leading and delivering AI projects going through scoping ambiguous problems, communicating trade-offs, and iterating based on real-world feedback
Extensive experience in Python and its core data science libraries (e.g., Scikit-learn, Pandas, NumPy, Matplotlib/Seaborn).
Hands-on experience building and taking LLM-powered applications to production (retrieval, agents, structured outputs, prompt safety)
Experience with Agentic AI frameworks and designing multi-step AI reasoning processes
Strong experience in full stack fundamentals and system design and integrations with demonstrated leadership and experience in designing and managing low latency microservices at scale withn authentication and authorization (experience in using OAuth 2.0, OpenID Connect, and SAML)
Experience with MLOps principles and tools for model versioning (e.g., Git), containerization (e.g., Docker), and continuous integration/continuous deployment (CI/CD) of machine learning models
Proven experience in automated testing and the ability to develop test strategies and design automation frameworks
Demonstrated expertise in feature engineering, feature selection, and data transformation techniques for structured and unstructured data

Nice To Haves

Strong theoretical and practical knowledge of classical machine learning algorithms (e.g., classification, regression, clustering, dimensionality reduction) and their applications in areas such as fraud detection, credit risk scoring, or customer segmentation
Experience in Fine Tuning LLMs, embedding models, and tackling advanced RAG or agentic use cases
Experienced with building and deploying NLP and voice response applications (including IVR and contact center intelligence)
Experience in designing AI observability, monitoring systems, feedback loops, and trend analysis
Familiarity with Google's Vertex AI tech stack
Experience building applications with modern web component frameworks (such as React & Angular)
Experience with containerized tooling and cloud platforms (particularly Docker/Docker Compose, Kubernetes, and GCP)

Responsibilities

Work with the business to define and implement a strategy for personalizing natural language interfaces
Act as lead engineering partner to translate business requirements into architectural patterns and best practices for agentic AI development
Partner with Data, security, risk, and engineering teams to embed AI-driven resiliency, observability, and threat modeling into architecture standards
Work as part of a team defining and delivering the strategy and technical components and patterns for an agentic AI ecosystem
Design and implement secure, resilient, and compliant AI platforms that integrate with existing enterprise systems, ensuring alignment with risk, ethics, and regulatory frameworks
Build on knowledge of event driven architecture, message queues, and distributed systems to build resilient and scalable workflows in a secure and compliant manner
Drive search and retrieval architecture, including vector-based search systems, indexing strategies, and relevance optimization
Architect and implement scalable services and user-facing workflows across frontend and backend using agentic skills and tools
Manage and iterate on new platform capabilities while balancing the needs of key projects delivering maintainable, scalable, testable, and easy to refactor
Work hands-on and de-risk the critical parts of our complex projects. The role will be deeply involved in coding, reviewing, and optimizing AI solution
Inspire and mentor teams across architecture and engineering disciplines, cultivating a culture of innovation, responsibility, and trust in AI adoption.
Drive next generation testing and observability practices for dynamic orchestration of user journeys
Championing a culture of excellence, inclusion, and continuous learning within the team. When something breaks, you're not satisfied with a workaround. You dig until you understand why, and you bring learning and long-term solutions back to the team.