Senior AI Engineer, Finance AI & Automation

Bank of Montreal • Chicago, IL

1d

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. Solutions are built on BMO’s enterprise AI platform and within its Finance AI Governance Program, with a focus on safety, auditability, and readiness for regulatory review. The engineer partners with leaders in FP&A, Controllership, Treasury, and Finance Operations on use cases such as variance analysis, reconciliations, close acceleration, policy research, and treasury forecasting.

The position combines application engineering, agentic AI and LLM development, production operations, and risk governance. The engineer builds front ends, backend services, and integrations; designs agent workflows and retrieval-augmented generation; establishes evaluation harnesses and acceptance thresholds; and works with Technology and AI Platform teams on security, CI/CD, resilience, observability, and support. The role also documents use cases and monitoring plans, applies data controls, supports adoption, develops reusable engineering patterns, reviews code, sets standards, and mentors analysts and junior engineers.

More broadly, the position researches, builds, and implements scalable AI and machine learning systems, improves data pipelines and lakes, evaluates and optimizes models, runs experiments, and creates repeatable analytics and decision-support solutions. It operates at a group- or enterprise-wide level, serving as a specialist resource to senior leaders and stakeholders. The role requires applying BMO’s Risk Management Framework and making sound, risk-informed decisions in accordance with applicable policies, laws, and regulations.

Requirements

• Typically 7 or more years of relevant professional experience, including at least 2 years building and deploying LLM or generative AI applications in production.
• Expert proficiency 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 equivalents.
• 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 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.
• 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, and collaboration 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, or an equivalent combination of education and experience.

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, and deploy end-to-end AI applications, including web front ends, backend services, APIs, and integrations 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.
• Implement 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 AI platform services, including the AI gateway, guardrails, registry, and observability, rather than creating parallel tooling.
• Instrument tracing, usage, cost, and drift monitoring, and respond to and resolve production issues.
• Complete AI use case documentation, testing evidence, and Ongoing Monitoring Plans, and 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.
• 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, set engineering standards, and mentor analysts and junior engineers.
• Research, build, and implement scalable artificial intelligence systems capable of learning and making predictions in response to 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 and models; train and retrain systems to prevent drift and optimize results.
• Collaborate 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 conduct statistical analysis and interpretation of results.
• Solve complex problems with multi-layered data sets, extend existing ML frameworks, and optimize machine learning libraries.
• Develop machine learning applications, implement algorithms, and build tools to apply ML frameworks.
• Turn unstructured data into useful information, including through image auto-tagging and text-to-speech conversions.
• Develop ML algorithms to analyze historical data and make predictions.
• Apply BMO’s Risk Management Framework, Risk Culture, and approved Risk Appetite in carrying out the role, protecting bank assets and following applicable policies, laws, and regulations.
• Take on broader work or accountabilities as needed.

Benefits

• Health insurance.
• Tuition reimbursement.
• Accident and life insurance.
• Retirement savings plans.
• The total compensation package may include performance-based incentives, discretionary bonuses, and other perks and rewards.
• Training and coaching, manager support, and network-building opportunities.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior