Staff AI Engineer – Tangerine, Toronto, ON
Location: Toronto, ON | Company: Scotiabank
Tangerine is hiring a Staff AI Engineer in Toronto, Ontario, for a senior technical position focused on designing, delivering, and scaling secure AI-powered applications across the bank.
This role combines hands-on engineering, technical leadership, architecture, stakeholder coordination, and responsible AI practices. The successful candidate will lead complex AI initiatives from early concept through production, with a strong focus on large language models, agentic workflows, retrieval-augmented generation, secure microservices, and enterprise-grade operational standards.
About the Staff AI Engineer Role
The Staff AI Engineer will serve as a technical lead for AI use cases within Tangerine, helping teams translate business needs into practical engineering plans, delivery milestones, and production-ready capabilities. The position requires ownership of technical decomposition, dependency management, implementation quality, and operational readiness.
A major part of the role involves building LLM-powered and agentic applications that may include retrieval-augmented generation, multi-step reasoning, structured outputs, prompt safety, and automated workflows. The engineer will also help define reusable patterns and architectural guidance that can be applied across future AI initiatives.
Key Areas of Responsibility
The position combines AI engineering, secure system design, technical leadership, production delivery, and collaboration with business, platform, security, and risk teams.
Agentic AI Delivery
Design, build, and productionize AI applications using large language models, retrieval systems, agent workflows, structured outputs, and prompt-safety techniques.
Technical Leadership
Own technical planning, delivery coordination, dependency management, and execution quality from initial business case through production rollout.
Secure System Architecture
Develop secure, resilient, observable, and low-latency services using modern authentication, authorization, and enterprise integration patterns.
Testing and Reliability
Build automated testing strategies, operational feedback loops, monitoring practices, and resilience controls for production AI systems.
Stakeholder Collaboration
Work with business, product, platform, security, and risk partners to align technical solutions with compliance, governance, and operational requirements.
Engineering Standards
Contribute reusable patterns, code-quality practices, architectural guidance, and technical standards across multiple AI initiatives.
Technical Experience and Qualifications
Applicants should have experience leading complex AI initiatives involving multiple stakeholders, with responsibility extending from early business case development through production launch. The role requires the ability to make sound technical decisions involving speed, scalability, risk, maintainability, and operational complexity.
Candidates must have extensive experience with Python and core data science libraries such as Scikit-learn, Pandas, NumPy, and Matplotlib. Hands-on experience building LLM-powered applications, agentic systems, retrieval workflows, structured outputs, and prompt-safety controls is also required.
Skills That May Help Candidates Succeed
This position may suit experienced engineers who can combine practical AI delivery with full-stack development, secure architecture, clear communication, and strong technical judgement.
Experience building retrieval systems, AI agents, multi-step reasoning workflows, and production-grade language-model applications is central to the role.
Strong Python skills and familiarity with established data science libraries can support experimentation, model integration, and reliable application development.
Production experience with OAuth 2.0, OpenID Connect, SAML, authentication, authorization, and microservices helps support secure enterprise deployment.
Knowledge of unit testing, functional testing, CI/CD, containerization, model versioning, and automation frameworks can improve delivery quality and maintainability.
The ability to guide peers through design discussions and code reviews can raise engineering standards and support long-term team development.
Preferred Technology Background
Experience acting as technical lead for an AI-driven product and working closely with product management may strengthen an application. Familiarity with agentic AI frameworks, multi-step reasoning, MLOps practices, Docker, Git, and continuous integration and deployment may also be beneficial.
Additional knowledge of classical machine learning, fraud detection, credit risk, customer segmentation, natural language processing, voice-response applications, contact-centre intelligence, Google Vertex AI, React, or Angular may help candidates contribute across a broader range of Tangerine initiatives.
How to Apply
Candidates should review the complete Tangerine posting before applying to confirm that their AI engineering experience, Python knowledge, production delivery background, security expertise, and technical leadership capabilities align with the position.
Applications must be submitted through Tangerine’s official careers website. Only candidates selected to continue in the recruitment process may be contacted.