Agentic AI Engineer

BMO•Toronto, ON

10d•Hybrid

About The Position

The team accelerates BMO’s AI journey by building enterprise-grade, cloud-native AI solutions, combining engineering excellence with cutting-edge AI to deliver scalable, secure, and responsible solutions that power business innovation across the bank. They enable and accelerate partners on their AI journeys, helping teams unlock value at scale. The team consists of engineers, AI practitioners, platform builders, thought leaders, multipliers, and coders, forming a global and diverse group focused on creating smart, secure, and scalable solutions. The ambition is to deploy capital and resources to their highest and most profitable use through a digital-first operating model, powered by data and AI-driven decisions. As an AI Engineer, you will contribute to a multi-year initiative to advance BMO's digital-first, AI-powered business. This role involves shaping and delivering agentic systems by integrating Large Language Models (LLMs) to orchestrate and automate business workflows, aiming for operational efficiency and optimized user experiences. You will be hands-on in solution design, demonstrate engineering excellence, and provide technical leadership for high-impact capabilities, ensuring robust and scalable AI solutions.

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

Certifications in Azure AI Engineer, python is a plus.
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.

Responsibilities

Drive the development of the “Agent Ecosystem” by designing, building, and operationalizing enterprise-grade AI agents and the orchestration layer that seamlessly coordinates their interactions.
Serve as a player-coach, balancing hands-on engineering, building agent prototypes and platform components, with strategic guidance, including shaping product direction, advising on implementation best practices, and fostering a culture of technical excellence.
Initially focus on creating foundational patterns and frameworks that can be leveraged across the broader agent development landscape, enabling scalability and reusability.
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.