Full Stack Engineer (AI-Enabled)

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:
• $70,000.00 - $150,000.00
• 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

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