BMO Financial Group is seeking a Senior AI Engineer to design, build, and operate production-grade AI solutions across Finance end to end. The role focuses on enterprise AI platform delivery, including user-facing interfaces and APIs, agentic workflows, retrieval and data integration, and evaluation and monitoring with appropriate governance for regulatory readiness.
Key Responsibilities
- Design, build, and deploy end-to-end AI applications, including web front ends (React/TypeScript), backend services and APIs (Python/FastAPI), and integration with Finance data and systems.
- Convert Finance business problems into technical designs and make trade-offs across accuracy, latency, cost, and maintainability.
- Develop multi-step agentic workflows using tool and function calling, MCP integrations, and orchestration frameworks.
- Implement retrieval-augmented generation (RAG) over Finance policies, documents, and structured data, including chunking, embeddings, vector search, and context engineering.
- Introduce human-in-the-loop controls for material judgments, approvals, and financial postings.
- Create evaluation harnesses using 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.
- Collaborate with Technology and AI Platform teams on CI/CD, security reviews, resilience, and support handover.
- Use shared enterprise platform services such as the AI gateway, guardrails, registry, and observability rather than developing parallel tooling.
- Instrument tracing, usage, cost, and drift monitoring; respond to and resolve production issues.
- Serve as the AI Developer under the Risk and Finance AI Governance Program by completing use case documentation, testing evidence, and Ongoing Monitoring Plans for items raised in Second Line reviews.
- Apply data classification, access controls, PII protection, and lineage in alignment 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.
Required Qualifications
- 7+ years of professional software engineering experience, including 2+ years building and deploying LLM or generative AI applications to production.
- Expert Python, and proficiency with a modern front-end framework (React/TypeScript preferred).
- Strong API design skills, plus SQL and data modeling expertise.
- Hands-on experience with agent orchestration frameworks (for example: LangGraph, Semantic Kernel, AutoGen or equivalent), RAG architectures, and vector databases, including prompt and context engineering.
- Experience with cloud AI services including Azure OpenAI / Azure AI Foundry and/or AWS Bedrock, along with containers, CI/CD, and observability tooling.
- Proven ability to evaluate non-deterministic systems and build automated test and evaluation pipelines.
- Experience working in a regulated environment with knowledge 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 related field, or equivalent experience.
- Finance domain knowledge in areas such as financial close, reconciliations, FP&A, treasury, liquidity, or IRRBB.
- Familiarity with model risk management guidance (for example: 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 (for example: GitHub Copilot) within a governed engineering workflow.
- Bilingual English/French is an asset.
Relevant Technologies
- React, TypeScript, Python, FastAPI
- MCP integrations
- LangGraph, Semantic Kernel, AutoGen
- RAG, vector databases, embeddings, vector search
- Azure OpenAI, Azure AI Foundry, AWS Bedrock
- Containers, CI/CD, observability tooling
- AI gateway, guardrails, registry, tracing
- Databricks, Snowflake, Power BI
- Oracle, Hyperion
- GitHub Copilot
Location
Chicago, IL (onsite)
Compensation
USD 120,000 - 250,000 per year. Pay type: Salaried.
Benefits
- Health insurance
- Tuition reimbursement
- Accident and life insurance
- Retirement savings plans
For additional details: https://jobs.bmo.com/global/en/Total-Rewards
Application Details
- Application deadline: 11/29/2026
- Address: 320 S Canal Street
- Job family group: Data Analytics & Reporting
Typical Finance AI Use Cases
- Variance analysis and commentary
- Reconciliations and manual journal entries
- Close acceleration
- Controllership and policy research
- Treasury forecasting
Role Context: Risk and Risk Management
Execution of the role requires taking measured risks while protecting the bank by applying BMO’s Risk Management Framework in line with its Risk Culture and approved Risk Appetite. Responsibilities include making sound, risk-informed decisions that align to business strategy, protect assets, and adhere to applicable policy documents (Frameworks, Policies, Standards, Procedures and Supporting documents) as well as laws and regulations.
