Vice President, AI Engineer

BMO Capital Markets is a leading, full-service financial services provider. We offer corporate and investment banking, treasury management, as well as research and advisory services to clients around the world. #bmocapitalmarkets

About The Role We are seeking a highly skilled AI Engineer to join the Data Cognition Team (DCT) at BMO Capital Markets . In this role, you will design, develop, and deploy next-generation AI systems with a strong focus on Agentic AI, AI Platforms, AI Harnesses, Generative AI, and Large Language Models (LLMs) .

You will work at the intersection of applied AI research and engineering, building scalable, secure, and production-grade AI solutions that enable autonomous workflows, intelligent decision-making, and enterprise-wide AI adoption across Investment Banking and Global Markets. This role is ideal for candidates who are passionate about advancing state-of-the-art AI capabilities and translating cutting-edge research into business value.

Our Team

The Data Cognition Team (DCT) develops and operates a scalable, customizable, and sustainable suite of AI-powered platforms and products that support multiple business units across Capital Markets. We leverage modern AI technologies, including Agentic AI, Generative AI, Retrieval-Augmented Generation (RAG), and multi-agent systems, to solve complex business challenges and drive innovation across Investment Banking, Global Markets, Research, and Corporate Functions.

Key Responsibilities

Agentic AI & AI Platform Development

• Design, develop, and maintain advanced Agentic AI systems, including multi-agent architectures that can reason, plan, collaborate, and execute complex workflows.
• Build enterprise-grade AI Harnesses and AI Engineering Platforms that support model experimentation, evaluation, deployment, observability, governance, and lifecycle management.
• Develop autonomous and semi-autonomous AI applications leveraging LLMs, RAG, tool-calling, and workflow orchestration frameworks.
• Design evaluation frameworks for AI agents, including benchmarking, safety testing, hallucination detection, and performance monitoring.

Architecture & Engineering

• Architect and deploy scalable AI solutions using microservices, APIs, containers, and cloud-native technologies.
• Implement distributed compute solutions and optimize large-scale AI workloads for performance, resiliency, and cost efficiency.
• Design inference pipelines and agent orchestration workflows to reduce latency and improve reliability.
• Build reusable AI services, SDKs, and components that accelerate enterprise AI adoption.

AI Governance, Security & Observability

• Apply Responsible AI, model governance, and risk management principles throughout the AI development lifecycle.
• Implement comprehensive observability, tracing, evaluation, and monitoring capabilities for AI systems and agents.
• Integrate privacy-preserving techniques, cybersecurity controls, and compliance requirements into AI solution architectures.
• Establish engineering best practices for secure, production-grade Agentic AI deployments.

Collaboration & Innovation

• Partner with business stakeholders, product owners, and technology teams to identify opportunities for AI-driven transformation.
• Contribute to AI strategy, architecture standards, and technology roadmaps.
• Stay current with emerging AI research, agent frameworks, LLM advancements, and industry best practices.

Qualifications

• PhD in Computer Science, Artificial Intelligence, Machine Learning, Engineering, Physics, Mathematics, or a related quantitative field with 3+ years of industry experience, OR
• Master’s degree in a related field with 5+ years of industry experience designing and deploying production AI systems.
• Strong software engineering skills, particularly in Python and modern AI/ML frameworks such as PyTorch and TensorFlow.
• Extensive experience with Large Language Models (LLMs), Generative AI, Retrieval-Augmented Generation (RAG), and advanced prompting techniques.
• Hands-on experience building Agentic AI systems, including planning, memory, tool usage, workflow orchestration, and multi-agent collaboration.
• Experience with AI orchestration frameworks such as LangGraph, CrewAI, AutoGen, BeeAI, Semantic Kernel, LangChain, or similar technologies.
• Experience developing AI Harnesses, evaluation frameworks, model benchmarking solutions, and AI obs…