Senior AI Engineer

Team Overview

We accelerate BMO’s AI journey by building enterprise‑grade, cloud‑native AI platforms and solutions. Our team combines strong engineering discipline with applied AI and Generative AI to deliver scalable, secure, and responsible capabilities that power business innovation across the bank.

We enable and accelerate our partners on their AI journeys across the enterprise by providing standardized platforms, architectures, and guardrails that make it easy to build, deploy, and operate AI solutions at scale. We are engineers, AI practitioners, platform builders, and multipliers who enjoy working together to create smart, secure, and scalable solutions that make a meaningful impact.

Our ambition is bold: deploy our capital and resources to their highest and most profitable use through a digital‑first operating model, powered by data‑ and AI‑driven decisions.

About the Role

As a Senior AI Engineer, you will play a hands‑on senior engineering role within BMO’s Azure cloud and Applied AI ecosystem. This role is primarily focused on engineering AI‑ready cloud platforms and systems on Azure, with additional responsibility for designing and enabling production‑grade AI and Generative AI systems.

You will design, build, and operate secure, scalable Azure infrastructure and platform services that support AI, Generative AI, and digital workloads, while also contributing directly to the implementation of AI systems such as LLM‑based services, agent‑based architectures, and model‑driven applications.

This role blends deep cloud and platform engineering with applied AI system design. You will work in agile, cross‑functional pods with cloud engineering, data engineering, AI/ML teams, and security and risk partners to evolve BMO’s AI platforms. As a senior engineer, you will influence technical direction, help define AI‑ready architecture standards, and mentor other engineers while remaining deeply hands‑on.

Key Responsibilities

Cloud Engineering & Platform Delivery

• Design, build, and operate Azure cloud infrastructure and shared platform services supporting AI, GenAI, data, and digital workloads.

• Implement and evolve Azure landing zones, subscriptions, networking, identity, and governance aligned with enterprise standards.

• Build cloud‑native solutions using Azure PaaS services, containers, and managed platforms.

• Ensure platforms meet requirements for scalability, resilience, performance, cost efficiency, and reuse.

• Apply DevOps and SRE practices, including CI/CD, monitoring, alerting, and operational readiness.

AI & Generative AI Systems Enablement

• Enable AI and Generative AI systems on Azure through secure, standardized platform capabilities.

• Design and implement cloud‑based architectures for LLM and SLM‑powered applications, including API‑driven and event‑driven patterns.

• Support AI system patterns such as agent‑to‑agent (A2A) interactions, model context protocols (MCP), and orchestration of AI workflows.

• Partner with AI and data teams to operationalize AI systems with production‑grade non‑functional requirements.

• Contribute to reference architectures and reusable components for enterprise AI and GenAI systems.

Infrastructure as Code & Automation

• Design and maintain Infrastructure as Code using Bicep, ARM, Terraform, or CDK‑based frameworks.

• Create reusable, modular deployment patterns that balance self‑service with governance.

• Automate environment provisioning, configuration, and policy enforcement across environments.

• Enable repeatable deployment of AI‑ready environments and services.

Security, Governance & Risk

• Apply security‑by‑design principles across identity, networking, encryption, secrets management, and auditability.

• Implement secure service‑to‑service authentication using managed identities and least‑privilege access.

• Ensure AI and cloud solutions comply with security, privacy, data governance, and regulatory requirements.

• Support architecture reviews, risk assessments, and audits in partnership with security and risk teams.

• Design platforms and AI systems with observability, traceability, and operational controls enabled by default.

Required Technical Skills & Competencies

Core Azure Cloud Engineering (Must‑have)

• Hands‑on experience engineering solutions on Microsoft Azure in large‑scale or regulated environments.

• Strong understanding of Azure networking, identity, access management, and governance.

• Experience designing for scalability, reliability, performance, and cost efficiency.

• Proven ability to troubleshoot and operate production Azure environments.

Infrastructure as Code & DevOps (Must‑have)

• Experience with Infrastructure as Code using Bicep, ARM, Terraform, or similar tools.

• Experience building and operating CI/CD pipelines for infrastructure and application delivery.

• Familiarity with IAM models, role definitions, and least‑privilege access patterns.

Cloud‑Native Integration & APIs (Must‑have)

• Experience with Azure API Management or similar API gateway solutions.

• Understanding of secure service exposure, authentication, rate limiting, and API lifecycle management.

• Experience designing service‑based and event‑driven architectures.

AI / GenAI Systems (Nice‑to‑Have)

• Experience enabling or building AI and Generative AI systems on Azure from an engineering perspective.

• Familiarity with LLM and SLM‑based application architectures, including orchestration and agent‑based patterns.

• Understanding of secure deployment of AI systems in regulated enterprise environments.

Reliability & Observability (Nice‑to‑Have)

• Experience implementing monitoring, logging, alerting, and operational dashboards.

• Familiarity with SRE concepts such as SLIs, SLOs, and resilience patterns.

Qualifications & Experience

• 4+ years of experience in cloud engineering, platform engineering, or AI‑adjacent engineering roles.

• Proven experience delivering and operating Azure‑based platforms or services in production.

• Experience supporting AI, analytics, or GenAI workloads is a strong asset.

• Bachelor’s degree in Computer Science, Engineering, or a related field (or equivalent practical experience).