Is this role right for you? In this role you will: Design, develop, and maintain reusable platform services for AI and machine learning workloads, including model training, evaluation, deployment, inference, monitoring, and lifecycle management.

Build scalable APIs, orchestration layers, SDKs, and self-service tooling that improve developer productivity and reduce friction for AI product teams.

Implement production-grade MLOps and LLMOps capabilities, including CI/CD pipelines, model and prompt versioning, automated testing, observability, drift detection, and rollback mechanisms.

Partner with data scientists, machine learning engineers, application teams, security, risk, and architecture stakeholders to translate AI use cases into secure, reliable platform capabilities.

Develop and operate containerized workloads using Kubernetes, Docker, cloud-native services, and infrastructure-as-code practices.

Support retrieval-augmented generation, vector search, agentic workflows, model serving, feature pipelines, and enterprise integration patterns where applicable.

Embed reliability, resiliency, scalability, cost optimization, governance, and security controls into platform design and day-to-day engineering practices.

Define and enforce engineering standards for code quality, testing, observability, documentation, operational readiness, and production support.

Evaluate emerging AI platform tools, frameworks, and cloud services, and recommend adoption patterns aligned with enterprise architecture standards.

Participate in incident response, root-cause analysis, performance tuning, and continuous improvement of platform services.

Do you have the skills that will enable you to succeed in this role? We'd love to work with you if you have: Bachelor’s degree in Computer Science, Software Engineering, Data Engineering, Machine Learning, or a related technical discipline, or equivalent practical experience. 3+ years of professional software engineering experience building production systems, platforms, APIs,



or distributed services.

Strong programming skills in Python, Java, Scala, or a similar language, with a focus on clean, testable, maintainable code.

Hands-on experience with cloud platforms such as Azure, AWS, or Google Cloud, including compute, storage, networking, identity, and managed AI or data services.

Experience with containerization and orchestration technologies such as Docker and Kubernetes.

Familiarity with MLOps, CI/CD, model deployment, experimentation tracking, observability, logging, monitoring, and automated testing practices.

Working knowledge of machine learning concepts, model serving patterns, data pipelines, feature engineering, or generative AI application patterns.

Experience building modern front-end applications using frameworks such as React, Angular, or Vue, with strong understanding of component-based design, state management, and responsive user interfaces.

Ability to develop intuitive, accessible, and performant user experiences for developer portals, dashboards, workflow tools, or self-service platform interfaces.

Experience applying secure software development practices, access controls, secrets management, and production operational controls.

Strong problem-solving, communication, collaboration, and ownership skills in a cross-functional engineering environment Nice‑to‑Have Qualifications: Experience building internal developer platforms, AI platforms, ML platforms, or shared engineering services at enterprise scale.

Experience with MLflow, Kubeflow, Airflow, Argo Workflows, Databricks, Ray, Spark, Feature Stores, vector databases, or model registries.





Experience with LLM application development, retrieval-augmented generation, prompt orchestration, evaluation frameworks, guardrails, or agentic AI workflows.

Familiarity with infrastructure-as-code tools such as Terraform, Bicep, ARM templates, Cloud

Formation, or Pulumi.

Experience operating services in regulated environments with governance, auditability, data privacy, and model risk management requirements.

Certifications such as Azure AI Engineer Associate, AWS Machine Learning Specialty, Google Professional Machine Learning Engineer, or Certified Kubernetes Administrator.

What's in it for you? Diversity, Equity, Inclusion & Allyship - We strive to create an inclusive culture where every employee is empowered to reach their fullest potential, respected for who they are, and are embraced through bias-free practices and inclusive values across Scotiabank.

We embrace diversity and provide opportunities for all employee to learn, grow & participate through our various Employee Resource Groups (ERGs) that span across diverse gender identities, ethnicity, race, age, ability & veterans.

Accessibility and Workplace Accommodations - 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.

Scotiabank continues to locate, remove and prevent barriers so that we can build a diverse and inclusive environment while meeting accessibility requirements.

Upskilling through online courses, cross-functional development opportunities, and tuition assistance.

Competitive Rewards program including bonus, versatile vacation, personal, sick days and benefits will start on day one.

Dynamic Ecosystem - Free tea & coffee, universal washrooms, and lots of space for team collaboration.

Community Engagement - No matter where you choose to work from; we offer opportunities for community engagement & belonging with our various programs.

📌 AI Full Stack Engineer (Toronto)
🏢 Scotiabank
📍 Toronto