Senior AI Platform Software Engineer

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Title: Senior AI Platform Software Engineer

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

The Role

The Senior AI Platform Software Engineer will lead the design, development, and operation of scalable software platforms that enable enterprise teams to build, deploy, monitor, and govern artificial intelligence and machine learning solutions in production. Operating at the intersection of software engineering, cloud infrastructure, MLOps, generative AI, and platform engineering, this role provides technical leadership for reusable, secure, and reliable platform capabilities. The successful candidate will shape architecture, establish engineering standards, mentor engineers, and partner across product, data, security, risk, and architecture teams to accelerate responsible AI adoption at enterprise scale.

Is this role right for you? In this role you will:

• Lead the architecture, design, development, and evolution of reusable platform services for AI and machine learning workloads, including training, evaluation, deployment, inference, monitoring, and lifecycle management.
• Own the technical design of scalable APIs, orchestration layers, SDKs, developer portals, and self-service tooling that improve developer productivity and enable consistent enterprise adoption.
• Define and implement production-grade MLOps and LLMOps patterns, including CI/CD, model and prompt versioning, automated evaluation, observability, drift detection, release controls, and rollback mechanisms.
• Translate complex and ambiguous AI use cases into secure, extensible platform capabilities in partnership with data scientists, machine learning engineers, application teams, security, risk, and architecture stakeholders.
• Design and operate resilient cloud-native and containerized services using Kubernetes, Docker, managed cloud services, and infrastructure-as-code practices.
• Provide architectural leadership for generative AI capabilities, including model serving, retrieval-augmented generation, vector search, agentic workflows, feature pipelines, evaluation, guardrails, and enterprise integration patterns.
• Embed reliability, scalability, security, privacy, governance, auditability, and cost optimization into platform architecture and engineering practices.
• Establish and champion engineering standards for code quality, testing, observability, documentation, API design, operational readiness, and production support.
• Lead technical reviews, evaluate emerging AI platform technologies, and influence build-versus-buy and adoption decisions aligned with enterprise strategy and architecture standards.
• Mentor engineers, provide hands‑on technical guidance, drive design consistency, and raise engineering maturity across platform teams.
• Lead incident response, root‑cause analysis, performance optimization, and continuous improvement for business‑critical 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.

5+ years of professional software engineering experience, including significant experience designing and delivering production platforms, APIs, or distributed systems.

Advanced programming skills in Python, Java, Scala, Go, or a similar language, with a strong record of producing clean, testable, maintainable, and secure code.

Demonstrated experience leading system design for scalable, highly available services, including API design, asynchronous processing, distributed systems, data stores, caching, and performance optimization.

Deep hands‑on experience with Azure, AWS, or Google Cloud across compute, storage, networking, identity, security, and managed AI or data services.

Strong experience with Docker, Kubernetes, infrastructure as code, CI/CD, release automation, and production operations.

Hands‑on experience implementing MLOps or LLMOps capabilities such as model deployment, experiment tracking, registries, automated evaluation, observability, monitoring, and lifecycle controls.

Strong working knowledge of machine learning systems, data and feature pipelines, model serving, generative AI application patterns, and the operational risks associated with production AI.

Experience building modern front‑end applications using React, Angular, or Vue, including component‑based architecture, state management, accessibility, and responsive user interfaces.

Ability to design intuitive and performant experiences for developer portals, operational dashboards, workflow tools, and self‑service platform interfaces.

Proven ability to apply secure software development practices, identity and access controls, secrets management, threat mitigation, and production operational controls.

Strong technical leadership, problem‑solving, communication, collaboration, and ownership skills, with experience influencing cross‑functional teams and mentoring engineers.

Experience architecting internal developer platforms, AI platforms, ML platforms, or shared engineering services adopted by multiple teams at enterprise scale.

Hands‑on experience with technologies such as MLflow, Kubeflow, Airflow, Argo Workflows, Databricks, Ray, Spark, feature stores, vector databases, model gateways, or model registries.

Experience designing LLM and agentic AI platforms, including retrieval‑augmented generation, prompt orchestration, tool integration, evaluation frameworks, guardrails, identity, and observability.

Expertise with infrastructure‑as‑code and GitOps tools such as Terraform, Bicep, ARM templates, CloudFormation, Pulumi, Helm, or Argo CD.

Experience defining platform reference architectures, technical roadmaps, software development standards, and reusable patterns across engineering teams.

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

Relevant certifications such as Azure AI Engineer Associate, AWS Certified Machine Learning Engineer, 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, flexible 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.

Location(s): Canada : Ontario : Toronto

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.