Vice President, AI Engineer

BMO•Toronto, ON

1d•$120,000 - $150,000•Onsite

About The Position

We are seeking a talented AI Engineer to join our Data Cognition Team at BMO Capital Markets. In this role, you will design, develop, and deploy advanced AI solutions with a focus on Agentic AI, Generative AI, and Large Language Models (LLMs). You will work on applied science and engineering aspects of AI, building scalable, secure, and high-performing systems that deliver value across Investment Banking and Global Markets. The Data Cognition Team (DCT) at BMO Capital Markets delivers a scalable, customizable, and sustainable suite of AI-enabled products for multiple business units. We leverage cutting-edge AI research and technologies to solve complex business challenges and drive strategic transformation across Investment Banking, Global Markets, and other divisions.

Requirements

Bachelor’s or Graduate degree in Computer Science, Engineering, Physics, or related technical field.
Strong programming skills in Python and experience with ML frameworks (TensorFlow, PyTorch).
Deep understanding of LLM architectures and applied generative AI techniques.
Familiarity with Agentic AI tools and orchestration frameworks (e.g., Langfuse, LangGraph, Milvus).
Experience with distributed computing, containerization (Docker, Kubernetes), and infrastructure management.
Knowledge of reinforcement learning and multi-agent systems.
Strong problem-solving and analytical skills with an engineering mindset.
Excellent communication skills for collaboration with technical and business stakeholders.

Nice To Haves

Financial domain knowledge: investment banking concepts, trading strategies, and financial data formats.
Certifications in AI engineering, cloud platforms, or cybersecurity.
Experience with privacy-preserving AI techniques and Responsible AI frameworks.

Responsibilities

Design, develop, and maintain Agentic AI and Generative AI solutions, including multi-agent systems for collaborative decision-making.
Implement distributed compute solutions for AI workloads, optimizing performance and scalability.
Architect and deploy microservice-based AI systems using containerization and infrastructure-as-code tools.
Integrate AI-driven data quality techniques and privacy-preserving technologies into solution designs.
Apply cybersecurity principles to ensure secure AI deployments, particularly for Agentic and Generative AI systems.
Optimize inference pipelines and parallelize processing to reduce latency and improve system performance.
Develop observability and monitoring solutions for AI models and systems, ensuring reliability and compliance.
Collaborate with stakeholders to understand business needs and translate them into AI-driven solutions.
Stay current with emerging AI research, tools, and best practices to continuously improve capabilities.