Senior Full-Stack Engineer (UI, AI)

Senior Full-Stack Engineer (UI, AI)
Location: Toronto / hybrid
You will build the application layer of a knowledge graph platform for a Tier-1 North American bank's
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 work in. Every edit made in the UI has to flow through governed backend pipelines, so strong
software engineering matters as much as AI experience.
Responsibilities
Design and build Python backend services and REST/Fast APIs, including event-driven
pipelines that react to UI edits (re-validation, re-indexing, impact analysis).
Build a responsive React and TypeScript front end, following the client's existing UI
standards.
Model and query graph data in Neo4j (Cypher), working with the knowledge graph architect
on schema, constraints and performance.
Integrate LLM services through an approved, model-agnostic gateway: prompt orchestration,
structured outputs, guardrails and evaluation hooks.
Implement SSO integration, role-based access (viewer, editor, steward, admin) and audit
logging.
Write tests, set up CI/CD, and deploy to on-premises environments through the client's
release process.
Work in a small cross-functional squad with data engineers, a KG architect and the client's
governance stakeholders; demo regularly and turn feedback into working software.
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 upload and state
management.
Hands-on experience integrating LLMs into applications: prompt design, structured or JSON
outputs, retrieval, and handling non-deterministic outputs safely.
Solid engineering practice: clean architecture, automated testing, code review, Git, CI/CD
and containerisation (Docker; Kubernetes a plus).
Experience shipping software into regulated or enterprise environments with SSO, access
control and audit requirements.
Nice to have
Neo4j or another graph database (Cypher, graph data modelling).
MCP servers, agent frameworks (LangGraph or similar) or tool-calling APIs.

Data governance, metadata management, lineage or data catalogue tools.
Familiarity with ontologies (OWL, SHACL, FIBO) or semantic web tooling.
Experience with relational databases (Oracle or PostgreSQL) and SQL.
SQL parsing or static code analysis.
Azure AI services; on-premises or VDI-based delivery.
Banking or capital markets domain exposure.