Senior AI Engineer, Finance AI & Automation

Application Deadline:

11/29/2026

Address:

320 S Canal Street

Job Family Group:

Data Analytics & Reporting

BMO is hiring a Senior AI Engineer to design, build and run production-grade AI solutions across Finance. You will own solutions end to end:

• •user experience and APIs
• •agentic workflows
• •retrieval and data integration
• •evaluation and ongoing monitoring

You will build on BMO's enterprise AI platform and within the Finance AI Governance Program. Your work will be safe and auditable, and it will hold up to regulatory review.

You will partner directly with leaders in FP&A, Controllership, Treasury and Finance Operations. Typical use cases include:

• •variance analysis and commentary
• •reconciliations and manual journal entries
• •close acceleration
• •controllership and policy research
• •treasury forecasting

This role is for engineers who have shipped AI to production, not only built prototypes.

Why join:

• •Shape how AI changes Finance at one of North America's largest banks, working with senior Finance leadership.
• •Build real solutions that go to production, on an enterprise AI platform designed for scale and governance.
• •Help set engineering and Responsible AI standards for the Finance AI function.

Key accountabilities

Solution engineering

• •Design, build and deploy end-to-end AI applications: web front ends (React/TypeScript), backend services and APIs (Python/FastAPI), and integration with Finance data and systems.
• •Translate Finance business problems into technical designs, and make sound trade-offs between accuracy, latency, cost and maintainability.

Agentic AI and LLM systems

• •Build multi-step agentic workflows using tool and function calling, MCP integrations, and orchestration frameworks.
• •Design retrieval-augmented generation (RAG) 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.

Evaluation and quality

• •Build evaluation harnesses with golden datasets to measure accuracy, consistency, groundedness and regression across model and prompt changes.
• •Document methodology, assumptions and limitations. Define acceptance thresholds with business owners.

Production readiness and operations

• •Partner with the Technology and AI Platform teams on CI/CD, security reviews, resilience and support handover.
• •Use the shared enterprise platform services (AI gateway, guardrails, registry, observability) rather than building parallel tooling.
• •Instrument tracing, usage, cost and drift monitoring. Respond to and resolve production issues.

Governance, risk and data controls

• •Act as the AI Developer under the Risk and Finance AI Governance Program: complete use case documentation, testing evidence and Ongoing Monitoring Plans address conditions raised in Second Line reviews
• •Apply data classification, access controls, PII protection and lineage, in line with the Data Risk Directive, RDARR principles and model risk expectations.

Adoption and leadership

• •Run demos and pilots, gather feedback, and track adoption and value-realization metrics.
• •Build reusable patterns, components and templates that speed up future Finance AI use cases.
• •Review code, set engineering standards, and mentor analysts and junior engineers.

Qualifications

Required

• •7+ years of professional software engineering experience, including 2+ years building and deploying LLM or generative AI applications to production.
• •Expert Python, plus proficiency in a modern front-end framework (React/TypeScript preferred).
• •Strong API design, SQL and data modeling skills.
• •Hands-on experience with: agent orchestration frameworks (e.g., LangGraph, Semantic Kernel, AutoGen or equivalent) RAG architectures and vector databases prompt and context engineering
• •Experience with cloud AI services (Azure OpenAI / Azure AI Foundry and/or AWS Bedrock), containers, CI/CD and observability tooling.
• •Proven ability to evaluate non-deterministic systems and build automated test and evaluation pipelines.
• •Experience in a regulated environment, with an understanding of auditability, explainability, security and data privacy.
• •Ability to communicate technical concepts clearly to non-technical Finance stakeholders and senior leaders.
• •Bachelor's degree in Computer Science, Engineering, Mathematics or a related field, or equivalent experience.

Preferred

• •Finance domain knowledge: financial close, reconciliations, FP&A, treasury, liquidity or IRRBB.
• •Familiarity with model risk management guidance (e.g., OSFI E-23, SR 11-7).
• •Experience with Databricks, Snowflake or Power BI, and with Finance systems such as Oracle or Hyperion.
• •Experience building MCP servers or enterprise tool integrations for agents.
• •Experience using AI-assisted development tools (e.g., GitHub Copilot) within a governed engineering workflow.
• •Bilingual English/French is an asset.

Researches, builds, and implements scalable artificial intelligence systems capable of learning and making predictions to business requirements. Enhances data pipelines and lakes to ensure data is clean, accurate, and optimized for machine learning models. Monitors, evaluates, and optimizes learning processes to continuously improve high-performance models. Works with other data and analytics professionals to optimize, refine, automate and scale analysis into repeatable analytics solutions and decision support tools.

• •Designs and develops machine learning (ML) and deep learning systems.
• •Runs machine learning tests and experiments. Trains and retrain systems to prevent drift and optimize results.
• •Solves complex problems with multi-layered data sets, extends existing ML frameworks and optimizes existing machine learning libraries.
• •Develops Machine Learning apps, implements algorithms, and builds tools to apply ML frameworks.
• •Turns unstructured data into useful information by auto-tagging images and text-to-speech conversions.
• •Develops ML algorithms to analyze huge volumes of historical data to make predictions.
• •Runs tests, performs statistical analysis, and interprets test results.
• •Operates at a group/enterprise-wide level and serves as a specialist resource to senior leaders and stakeholders.
• •Applies expertise and thinks creatively to address unique or ambiguous situations and to find solutions to problems that can be complex and non-routine.
• •Implements changes in response to shifting trends.
• •Broader work or accountabilities may be assigned as needed.
• •Take measured risks while protecting the bank by applying our Risk Management Framework in the execution of your role, in line with our Risk Culture and within our approved Risk Appetite, making sound and risk informed decisions that align to business strategy, protect assets, and adhere to applicable policy documents (Frameworks, Policies, Standards, Procedures and Supporting documents), laws and regulations.

Qualifications:

Intermediate level of proficiency:

Systems Thinking.

Advanced level of proficiency:

• •Mathematics, Statistics & Operations Research.
• •Critical thinking.
• •Creative reasoning.
• •Computational Thinking and Programming.
• •Deep Learning.
• •Machine Learning.
• •Scaling Models.
• •Continuous Integration and Continuous Delivery/Deployment.
• •ML algorithm.
• •Verbal & written communication skills.
• •Analytical and problem solving skills.
• •Influence skills.
• •Collaboration & team skills; with a focus on cross-group collaboration.
• •Able to manage ambiguity.
• •Data driven decision making.
• •Typically 7+ years of relevant experience and post-secondary degree in related field of study or an equivalent combination of education and experience.
• •Seasoned professional with a combination of education, experience and industry knowledge.

Salary :

$120,000.00 - $250,000.00

Pay Type:

Salaried

The above represents BMO Financial Group’s pay range and type.

Salari