AI Developer & Integration Specialist
Job Description & How to Apply Below
Req : 47033
Faculty/Division: Ofc of the Chief Information Officer
Department: Enterprise Infrastructure Solutions
Campus:
St. George (Downtown Toronto)
Position Number:
Existing Vacancy: Yes
Description:
About us
The Enterprise Infrastructure Solutions (EIS) group, part of the Information Technology Services (ITS) division, is responsible for campus core network, campus wireless, wide area network connectivity and internet connectivity for the University, including connectivity to research and education networks.
EIS is also responsible for services related to departmental network management, network, server and storage infrastructure, Windows and Linux server management services, database and application integration and support, enterprise backup service, 24/7 operation of central administrative data centres and telecommunications services.
If you’re motivated and passionate about learning technologies and dedicated to improving experiences for today’s student, consider a career with us.
Your opportunity
Reporting to the Manager, AI Engineering & Operations within the Enterprise Infrastructure Solutions group, the AI Developer & Integration Specialist plays a critical role in developing, customizing and integrating AI applications, models and solutions into the University’s infrastructure and services. This role acts as a critical bridge between AI research and innovation and its practical, value-driven deployment, ensuring that all AI tools are usable, ethical and aligned with institutional goals.
Your responsibilities will include
Architecting and engineering complex, university-wide AI solutions, designing custom Retrieval-Augmented Generation (RAG) pipelines and autonomous agent workflows that meet strict institutional scalability and performance requirements.
Designs and maintains robust MLOps and CI/CD pipelines (using Git Hub Actions/Git Lab), making authoritative decisions on the infrastructure code that governs model training, deployment, and versioning.
Evaluates and selects appropriate foundational models (Open Source vs. Proprietary) and hosting strategies (Azure AI Foundry, AWS Bedrock, local GPU/TPU), directly influencing the University's cloud spend and computational resource efficiency.
Acts as the primary technical authority for AI security, implementing the OWASP Agentic AI framework to proactively identify and mitigate novel threats (e.g., prompt injection, insecure plugin design) before systems reach production.
Establishes and enforces the Model Context Protocol (MCP) standards for the University, ensuring a secure and standardized method for AI tools to access institutional data.
Engineers complex authentication flows using OIDC, OAuth2, and SAML, ensuring that AI agents inherit correct user permissions and preventing unauthorized data exfiltration or privilege escalation.
Conducts threat modeling for new AI integrations, serving as the 'gatekeeper' for approval on high-risk use cases involving sensitive or confidential data.
Establishes the University’s observability standards for AI, implementing Open Telemetry and tracing tools (e.g., Langfuse, MLFlow) to gain deep visibility into non-deterministic agent behavior and latency.
Designs rigorous evaluation frameworks ('Evals') to systematically test models for accuracy, hallucination rates, and bias, creating the benchmarks against which all internal AI tools are measured.
Solves unprecedented technical challenges related to stochastic software behavior, acting as the highest-level escalation point for complex debugging and performance optimization.
Essential Qualifications
Bachelor’s degree in computer science, Software Engineering, Engineering, Data Science or acceptable combination of equivalent experience.
Eight years or more years of experience in software development
Three to five years of experience in software development with a specific focus on AI/ML, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG).
Demonstrated experience in developing and integrating AI/ML models in an enterprise environment (e.g., RAG pipelines, vector databases, and secure function calling for AI agents).
Exper…
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