Vice President, AI Engineer

BMO•Toronto, ON

2d•CA$120,000 - CA$150,000

About The Position

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. 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.

Requirements

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 observability platforms.
Strong understanding of distributed computing, microservices architecture, APIs, Docker, Kubernetes, and cloud-native development.
Experience implementing production-grade AI governance, monitoring, security, and Responsible AI practices.
Strong analytical, problem-solving, and communication skills.

Nice To Haves

Research publications, patents, or demonstrated contributions in AI, machine learning, Agentic AI, or LLM-related domains.
Experience with vector databases and knowledge platforms such as Milvus, Weaviate, Pinecone, OpenSearch, or Azure AI Search.
Experience with AI observability and evaluation platforms such as Langfuse, Arize, Weights & Biases, MLflow, Phoenix, or similar tools.
Knowledge of reinforcement learning, reasoning systems, multi-agent coordination, and AI planning techniques.
Experience building enterprise AI platforms supporting hundreds or thousands of users.
Experience working within regulated industries such as financial services.
Knowledge of Capital Markets, Investment Banking, Trading, Research, and Financial Data domains.
Experience with Responsible AI, Model Risk Management, and AI Governance frameworks.
Certifications in AI engineering, machine learning, cloud platforms (AWS, Azure, GCP), or cybersecurity.
Experience contributing to open-source AI projects or internal AI platform initiatives.

Responsibilities

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.
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.
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.
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.