Quick overview
SeniorityMid Senior
Work modeHybrid
LocationToronto, ON, United States
Posted23 hours ago
DjangoDockerFastAPIFlaskSQLGitPythonReactKubernetesTypeScriptCI/CDLangGraph
Job description
Full Stack AI Engineer – Python, LangGraph & React
Location: Toronto, ON - Hybrid
Domain: Banking and Financial Service
About the role
You will build the application layer of a knowledge graph platform for a Tier-1 North American bank and its data governance team.
The platform turns legacy SQL code into business-readable lineage, links data elements and critical data elements (CDEs) to an industry ontology (FIBO), and lets business, data governance and technical users explore, enrich and govern that knowledge through a web app, chat and APIs for AI agents.
You will own both sides of the application: the backend services and pipelines, and the front end users interact with.
You will own both sides of the application: the backend services and pipelines, and the front end users interact with.
Every edit made in the UI must flow through governed backend pipelines, so strong software engineering matters as much as AI experience.
Must have
• 6+ years of professional software engineering, with at least 3 years building full-stack production applications.
• Strong Python backend skills (FastAPI, Django or Flask): API design, async processing, background jobs and workflow logic.
• Strong React and TypeScript, including complex forms, data grids, file uploads, and state management.
• Hands-on experience integrating LLMs into applications: prompt design, structured or JSON outputs, retrieval, and safely handling non-deterministic outputs.
• Solid engineering practice: clean architecture, automated testing, code review, Git, CI/CD and containerisation (Docker; Kubernetes a plus).
• 6+ years of professional software engineering, with at least 3 years building full-stack production applications.
• Strong Python backend skills (FastAPI, Django or Flask): API design, async processing, background jobs and workflow logic.
• Strong React and TypeScript, including complex forms, data grids, file uploads, and state management.
• Hands-on experience integrating LLMs into applications: prompt design, structured or JSON outputs, retrieval, and safely handling non-deterministic outputs.
• Solid engineering practice: clean architecture, automated testing, code review, Git, CI/CD and containerisation (Docker; Kubernetes a plus).
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