Bank of Montreal

Full Stack Engineer (AI-Enabled)

Bank of Montreal$75K — $141K *
Information Technology
5 - 7 years of experience
Job Overview by Ladders

Qualifications

  • 5-10 years of experience in backend or full stack development
  • Proficiency in a backend language (Java, Node.js, Python) and a frontend framework (React or Angular)
  • Strong understanding of system design and API development
  • Hands-on experience with large language models in production settings
  • Excellent problem-solving and communication skills
  • Ability to work autonomously and manage end-to-end delivery effectively

Responsibilities

  • Design and develop end-to-end applications across various tech layers
  • Translate business needs into scalable technical solutions
  • Own features from initial design to production deployment
  • Collaborate with cross-functional teams to address complex domain challenges
  • Build reliable microservices and APIs
  • Create user-friendly frontend applications
  • Ensure compliance and security in banking systems

Benefits

  • Comprehensive health insurance
  • Tuition reimbursement opportunities
  • Accidental and life insurance coverage
  • Retirement savings plans
  • Access to performance-based incentives and discretionary bonuses
Full Job Description
Application Deadline:

08/30/2026

Address:
401 9th Avenue SW, Suite 1930

Job Family Group:

Technology

Role Overview

We are looking for a Full Stack Engineer who can design and deliver scalable, enterprise-grade solutions while building AI capabilities directly into our platforms.

This role is not limited to a specific tech stack. We are looking for someone who:
  • Understands banking and business workflows
  • Takes end-to-end ownership of delivery
  • Is a strong problem solver
  • Has hands-on experience building with large language models and can apply them responsibly in a regulated environment


You will work across frontend, backend, and integration layers to build reliable systems, while delivering AI-enabled capabilities where they create genuine business value. Candidates are not expected to have a research background in machine learning. We are looking for engineers who have implemented LLM-based functionality, understand its practical limitations, and can build around them. There will be opportunity to develop deeper expertise in areas such as agentic systems, orchestration, and model evaluation, supported by structured learning and meaningful project work.

What You'll Work On
  • Build and enhance core banking and client onboarding platforms
  • Design APIs and backend services that support complex, multi-step workflows
  • Develop user-facing applications with a strong focus on usability and performance
  • Work on data-driven systems, integrations, and orchestration layers
  • Deliver AI-assisted capabilities including document processing, intelligent onboarding workflows, search and summarization, and decision support
  • Implement retrieval-augmented generation (RAG) capabilities that make internal policy, product, and client information accessible and actionable
  • Contribute to modernization initiatives including API-first design, cloud adoption, and event-driven architecture


Key Responsibilities
  • Design, build, and support end-to-end applications across frontend, backend, and integration layers
  • Translate business requirements into scalable technical solutions
  • Take ownership of features from design through production and support
  • Collaborate with product, architecture, and business teams to solve complex domain problems
  • Build robust microservices and APIs using Java, Node.js, or Python
  • Develop intuitive frontend applications using React or Angular
  • Ensure systems are secure, resilient, and compliant, which is critical in banking
  • Identify opportunities to leverage AI development tools to improve delivery efficiency
  • Contribute to continuous improvement of engineering practices covering quality, CI/CD, and observability
  • Develop features using LLM APIs, including prompt design, structured output handling, tool calling, and management of errors and edge cases
  • Implement and optimize RAG pipelines covering chunking strategy, embeddings, vector search, and grounding of responses in source documents
  • Establish validation and evaluation practices to assess AI output quality and identify regressions before release
  • Apply appropriate safety and security controls including input validation, PII handling, prompt injection mitigation, and human-in-the-loop review for material decisions
  • Manage cost and latency considerations associated with model usage


Core Technical Stack
  • Backend: Java (Spring Boot), Node.js, Python
  • Frontend: Angular or React
  • AI / LLM: LangChain, AWS Bedrock, Azure OpenAI, Copilot Studio, OpenAI and Anthropic APIs
  • Retrieval: Vector databases including pgvector, OpenSearch or Pinecone, or Chroma
  • Architecture: Microservices, APIs, event-driven systems
  • Data: SQL/NoSQL, Oracle
  • Infrastructure: AWS/Azure, Docker, Kubernetes, Kafka
  • Tools: CI/CD pipelines, observability platforms
  • Candidates are not expected to have experience across the full stack listed. Demonstrated strength in several areas and the ability to learn others is sufficient.


Must-Have Skills
  • 5-10 years of experience in backend or full stack development
  • Proficiency in at least one backend language (Java, Node.js, or Python) and one frontend framework (React or Angular)
  • Strong understanding of system design, API development, and building for scale
  • Experience delivering production-grade applications end-to-end
  • Strong problem-solving and analytical skills
  • Demonstrated ownership and accountability for delivery
  • Ability to work across tech stacks and adapt quickly
  • Strong communication and collaboration skills, including the ability to engage non-technical stakeholders
  • Hands-on experience building applications with large language models, with the ability to discuss design decisions, challenges encountered, and lessons learned
  • Practical understanding of prompt engineering and methods for evaluating output quality
  • Familiarity with RAG concepts including embeddings, vector search, and the relationship between retrieval quality and output quality
  • Exposure to agentic AI, including agents capable of planning and tool use, multi-agent workflows, and human-in-the-loop approval patterns
  • Experience with LangChain or LangGraph, AWS Bedrock, or Copilot Studio
  • Experience using AI development tools such as Copilot and code assistants within a team setting
  • Production experience supporting an LLM-based feature serving end users


Nice to Have
  • Experience in banking or financial services, particularly onboarding, payments, lending, trade, or KYC/AML. Candidates from other regulated or complex domains will also be considered
  • Awareness of AI security considerations including prompt injection, data leakage, and output validation, with reference to the OWASP LLM Top 10
  • Experience with model evaluation frameworks or LLM observability tooling
  • Familiarity with search, data retrieval, or analytics platforms
  • Experience with cloud-native or modernization programs
  • Understanding of event-driven or streaming architectures


Mindset We're Looking For
  • Thinks beyond code, focusing on business outcomes and user value
  • Owns problems end-to-end, not just assigned tasks
  • Comfortable working in ambiguous problem spaces
  • Willing to challenge existing approaches and think creatively
  • Applies considered judgement to AI, distinguishing between promising capability and production readiness
  • Risk-aware, recognizing that probabilistic outputs require appropriate controls in a banking context
  • Continuously learning, including how AI can enhance both products and engineering productivity
  • Pragmatic, balancing innovation with real-world delivery, cost, and compliance constraints


Salary:

$75,900.00 - $141,900.00

Pay Type:

Salaried

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

Salaries will vary based on factors such as location, skills, experience, education, and qualifications for the role, and may include a commission structure. Salaries for part-time roles will be pro-rated based on number of hours regularly worked. For commission roles, the salary listed above represents BMO Financial Group's expected target for the first year in this position.

BMO Financial Group's total compensation package will vary based on the pay type of the position and may include performance-based incentives, discretionary bonuses, as well as other perks and rewards. BMO also offers health insurance, tuition reimbursement, accident and life insurance, and retirement savings plans. To view more details of our benefits, please visit: https://jobs.bmo.com/global/en/Total-Rewards

About Bank of Montreal

The Bank of Montreal is a Canadian multinational investment bank and financial services company. It provides a wide range of personal and commercial banking, wealth management, and investment banking products and services. The bank had revenues of CAD 23.6 billion in 2020.
Learn more about Bank of Montreal
Size
45,454 employees
Market Cap
$60.9 billion
Industry
Founded
1817
5 Year Trend
+9.1%
NASDAQ

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