Agentic AI Engineer

BMO•Toronto, ON

11d•CA$75,900 - CA$141,900•Hybrid

About The Position

As an AI Engineer, you will contribute to a multi-year initiative dedicated to advancing our digital-first, AI-powered business for enhanced value and future readiness. In this pivotal role, you will help shape and deliver agentic systems by integrating Large Language Models (LLMs) to orchestrate and automate business workflows, driving operational efficiency and optimizing user experiences. You will be hands-on in solution design, demonstrate engineering excellence, and provide technical leadership across high-impact capabilities, ensuring robust and scalable AI solutions for our organization.

Requirements

5-7 years of AI software engineering experience, with 3+ years in AI/ML engineering, AI agent development, multi-agent systems.
Deep, hands-on experience across Microsoft Azure services (designing, deploying, and operating cloud-native systems).
Strong background in AI agent ecosystems (multi-agent patterns, orchestration concepts, agent registries, tool routing, memory/state, evaluation approaches).
Demonstrated ability to implement monitoring/observability for AI/agent solutions (logging, tracing, metrics, and operational alerting).
Proven delivery on multiple AI initiatives—comfortable shaping ambiguity into “the right questions,” crisp requirements, and practical design.

Nice To Haves

Experience with Azure AI Foundry / Microsoft “Foundry” tooling in AI solution enablement and governance/tuning workflows.
Experience with Applied AI Evals frameworks, agent governance standards, and operational controls in regulated or enterprise environments.
Familiarity with agent taxonomy/labeling approaches and how to apply them to scale standardized development across teams.
Background in designing enterprise-grade platform layers (identity, access controls, registry/source-of-truth patterns) for agents.
Financial services or wealth management experience preferred.
Certifications in Azure AI Engineer, python is a plus.

Responsibilities

Design and implement an agent orchestration layer (routing, tool-calling patterns, workflow coordination, agent registry integration, state management, and failure/fallback strategies).
Define and apply enterprise agent patterns (standard agent templates, reusable components, and orchestration controls).
Establish observability/monitoring for agents and orchestrations: logging, tracing, drift detection signals, agent-specific metrics, and operational dashboards.
Integrate Microsoft Azure services and Microsoft ecosystem components (with emphasis on Azure AI capabilities and “Foundry” experience where applicable).
Partner with leadership to clarify expected outcomes/vision and translate them into an executable build plan, architecture decisions, and delivery milestones.
Operate and support production grade AI solutions to meet availability, reliability, and performance expectation.
Perform routine model, prompt, and configuration updates within approved change processes.
Embed Applied AI Evals considerations into the platform: governance hooks, auditability, risk controls, and operational readiness for agents.