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Bank of Montreal

Runs Canadian and US banks for people and firms

Senior AI Engineer - Finance AI & Automation

AI Application Engineering +1Posted on Nov 9
Full-timeChicago, IL, USAIn-person

About the Job

Requirements

  • At least 7 years of professional software engineering experience, including at least 2 years building and deploying large language model or generative artificial intelligence applications to production.
  • Expert Python proficiency and proficiency in a modern front-end framework; React and TypeScript are preferred.
  • Strong application programming interface design, SQL, and data modeling skills.
  • Hands-on experience with agent orchestration frameworks such as LangGraph, Semantic Kernel, or AutoGen; retrieval-augmented generation architectures; vector databases; and prompt and context engineering.
  • Experience with cloud artificial intelligence services such as Azure OpenAI, Azure AI Foundry, or AWS Bedrock, as well as containers, continuous integration and continuous delivery, and observability tooling.
  • Ability to evaluate non-deterministic systems and build automated testing and evaluation pipelines.
  • Experience in a regulated environment, including understanding of auditability, explainability, security, and data privacy.
  • Ability to communicate technical concepts to non-technical Finance stakeholders and senior leaders.
  • Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field, or equivalent experience.
  • Intermediate proficiency in systems thinking.
  • Advanced proficiency in mathematics, statistics and operations research, critical thinking, creative reasoning, computational thinking and programming, deep learning, machine learning, model scaling, continuous integration and continuous delivery/deployment, machine learning algorithms, verbal and written communication, analytical and problem-solving skills, influence, cross-group collaboration, managing ambiguity, and data-driven decision-making.
  • Post-secondary degree in a related field of study or an equivalent combination of education and experience.
  • Ability to apply the Risk Management Framework and approved Risk Appetite, make sound risk-informed decisions, protect bank assets, and comply with applicable policies, standards, procedures, laws, and regulations.

Responsibilities

  • Design, build, and deploy end-to-end artificial intelligence applications, including web front ends, backend services and APIs, and integration with Finance data and systems.
  • Translate Finance business problems into technical designs and balance accuracy, latency, cost, and maintainability.
  • Build multi-step agentic workflows using tool and function calling, MCP integrations, and orchestration frameworks.
  • Design retrieval-augmented generation over Finance policies, documents, and structured data, including chunking, embeddings, vector search, and context engineering.
  • Embed human-in-the-loop controls for material judgments, approvals, and financial postings.
  • Build evaluation harnesses with golden datasets to measure accuracy, consistency, groundedness, and regression across model and prompt changes.
  • Document methodology, assumptions, and limitations, and define acceptance thresholds with business owners.
  • Partner with Technology and AI Platform teams on continuous integration and delivery, security reviews, resilience, and support handover.
  • Use shared enterprise platform services, including the AI gateway, guardrails, registry, and observability, rather than building parallel tooling.
  • Instrument tracing, usage, cost, and drift monitoring, and respond to and resolve production issues.
  • Complete use-case documentation, testing evidence, and Ongoing Monitoring Plans as the AI Developer under the Risk and Finance AI Governance Program, addressing conditions raised in Second Line reviews.
  • Apply data classification, access controls, personally identifiable information protection, and lineage in line with the Data Risk Directive, RDARR principles, and model risk expectations.
  • Run demos and pilots, gather feedback, and track adoption and value-realization metrics.
  • Build reusable patterns, components, and templates to accelerate future Finance AI use cases.
  • Review code, set engineering standards, and mentor analysts and junior engineers.
  • Research, build, and implement scalable artificial intelligence systems that learn and make predictions for business requirements.
  • Enhance data pipelines and lakes to ensure data is clean, accurate, and optimized for machine learning models.
  • Monitor, evaluate, and optimize learning processes to improve model performance.
  • Work with data and analytics professionals to optimize, refine, automate, and scale analysis into repeatable analytics solutions and decision-support tools.
  • Design and develop machine learning and deep learning systems; run tests and experiments; and train and retrain systems to prevent drift and optimize results.
  • Solve complex problems with multi-layered datasets, extend existing machine learning frameworks, and optimize machine learning libraries.
  • Develop machine learning applications, implement algorithms, and build tools to apply machine learning frameworks.
  • Convert unstructured data into useful information through tasks such as image auto-tagging and text-to-speech conversion.
  • Develop machine learning algorithms to analyze large volumes of historical data and make predictions.
  • Run tests, perform statistical analysis, and interpret test results.
  • Operate at a group or enterprise-wide level and serve as a specialist resource to senior leaders and stakeholders.
  • Apply expertise and creative problem-solving to unique or ambiguous situations and complex, non-routine problems.
  • Implement changes in response to shifting trends.
  • Perform other broader work or accountabilities as assigned.

Desired Qualifications

  • Finance domain knowledge, including financial close, reconciliations, financial planning and analysis, treasury, liquidity, or interest rate risk in the banking book.
  • Familiarity with model risk management guidance such as OSFI E-23 or SR 11-7.
  • Experience with Databricks, Snowflake, or Power BI, and Finance systems such as Oracle or Hyperion.
  • Experience building MCP servers or enterprise tool integrations for agents.
  • Experience using AI-assisted development tools such as GitHub Copilot within a governed engineering workflow.
  • Bilingual English and French language ability.

About the Company

Bank of Montreal

Runs Canadian and US banks for people and firms

Bank of Montreal, known as BMO, runs a Canadian bank and a large American bank, serving households and businesses on both sides of the border. Until the Bank of Canada opened in 1935, it acted as the Canadian government's banker. People use its branches and apps for accounts, mortgages, credit cards, and investments, while companies borrow from it and turn to its BMO Capital Markets arm to raise money and trade securities. BMO makes money on the gap between the interest it charges on loans and pays on deposits, plus fees from managing wealth and advising on deals. Bank of Montreal was founded in 1817 as Canada's first bank, and it keeps its legal head office in Montreal and its executive offices in Toronto, Canada.

Company size
10,001+
Company stage
IPO
Headquarters
Toronto, Canada
Founded
1817

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