Senior AI Engineer, Finance AI & Automation

BMO•Chicago, IL

1d•$120,000 - $250,000

About The Position

BMO is hiring a Senior AI Engineer to design, build, deploy, and operate production-grade AI solutions across Finance. The role owns solutions end to end, including user experiences and APIs, agentic workflows, retrieval and data integration, evaluation, and ongoing monitoring. The engineer will build on BMO’s enterprise AI platform and work within the Finance AI Governance Program to ensure solutions are safe, auditable, and suitable for regulatory review. The role partners with leaders in FP&A, Controllership, Treasury, and Finance Operations. Example use cases include variance analysis and commentary, reconciliations and manual journal entries, close acceleration, controllership and policy research, and treasury forecasting. The position is intended for an engineer with experience shipping AI systems to production. The Senior AI Engineer designs end-to-end AI applications, including React/TypeScript front ends, Python/FastAPI backend services and APIs, and integrations with Finance data and systems. The role builds agentic workflows and retrieval-augmented generation systems, incorporates human review for material judgments and financial postings, and develops evaluation methods to measure quality and regression. It also supports production readiness, operations, governance, risk and data controls, adoption, and engineering leadership. The role acts as the AI Developer under the Risk and Finance AI Governance Program, completing use case documentation, testing evidence, and ongoing monitoring plans, including addressing conditions raised in Second Line reviews. It applies data classification, access controls, PII protection, and lineage in line with applicable data-risk, RDARR, and model-risk expectations. The position also includes broader enterprise-level machine-learning responsibilities: designing and developing ML and deep-learning systems, conducting experiments and statistical analysis, monitoring and optimizing models, improving data pipelines, and turning analysis into repeatable decision-support solutions. The role operates as a specialist resource to senior leaders and stakeholders and applies BMO’s Risk Management Framework, Risk Culture, and approved Risk Appetite in its work.

Requirements

At least 7 years of professional software engineering or relevant experience.
At least 2 years building and deploying LLM or generative AI applications to production.
Expertise in Python and proficiency in a modern front-end framework; React/TypeScript is preferred.
Strong API design, SQL, and data-modeling skills.
Hands-on experience with agent orchestration frameworks such as LangGraph, Semantic Kernel, AutoGen, or equivalent.
Hands-on experience with RAG architectures, vector databases, and prompt and context engineering.
Experience with cloud AI services such as Azure OpenAI/Azure AI Foundry and/or AWS Bedrock, containers, CI/CD, and observability tooling.
Ability to evaluate non-deterministic systems and build automated test and evaluation pipelines.
Experience in a regulated environment and understanding of auditability, explainability, security, and data privacy.
Ability to explain 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.
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; scaling models; continuous integration and continuous delivery/deployment; and ML algorithms.
Verbal and written communication, analytical and problem-solving, influence, collaboration, and team skills, with a focus on cross-group collaboration.
Ability to manage ambiguity and make data-driven decisions.
Seasoned professional with a combination of education, experience, and industry knowledge.

Nice To Haves

Finance domain knowledge, including financial close, reconciliations, FP&A, treasury, liquidity, or IRRBB.
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/French.

Responsibilities

Design, build, deploy, and operate end-to-end AI applications, including web front ends, backend services, APIs, and Finance-system integrations.
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 and implement 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.
Build evaluation harnesses and golden datasets to measure accuracy, consistency, groundedness, and regression across model and prompt changes.
Document evaluation methodology, assumptions, and limitations, and define acceptance thresholds with business owners.
Partner with Technology and AI Platform teams on CI/CD, security reviews, resilience, and support handover.
Use shared enterprise platform services, including 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 governance documentation, testing evidence, and ongoing monitoring plans, and address conditions raised in Second Line reviews.
Apply data classification, access controls, PII protection, and data lineage in accordance with relevant data-risk and model-risk expectations.
Run demos and pilots, gather feedback, and track adoption and value-realization metrics.
Build reusable patterns, components, and templates for future Finance AI use cases.
Review code, establish engineering standards, and mentor analysts and junior engineers.
Design and develop machine-learning and deep-learning systems, apps, algorithms, and tools.
Run machine-learning tests and experiments; train and retrain systems to prevent drift and optimize results.
Enhance data pipelines and lakes to ensure data is clean, accurate, and optimized for machine-learning models.
Solve complex problems with multi-layered datasets, extend ML frameworks, and optimize machine-learning libraries.
Analyze large volumes of historical data to make predictions; run tests, perform statistical analysis, and interpret results.
Collaborate with data and analytics professionals to automate, refine, and scale analysis into repeatable analytics solutions and decision-support tools.
Apply the Risk Management Framework and make sound, risk-informed decisions aligned with business strategy, approved Risk Appetite, and applicable policies, laws, and regulations.
Take on broader work or accountabilities as assigned.