Full Stack Engineer (AI-Enabled)

BMO•Calgary, AB

2d•CA$75,900 - CA$141,900

About The Position

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, and 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.

Requirements

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 Haves

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

Responsibilities

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
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