Artificial Intelligence Systems Developer, Enterprise Architecture Data Management

Artificial Intelligence Systems Developer, Enterprise Architecture Data Management

Summary:

The artificial intelligence (AI) systems developer is the primary builder for AI enablement initiatives, responsible for translating roadmap commitments into production‑ready integrations, workflows, and tools. This role designs, builds, and deploys institutional AI services that deliver measurable value across administrative, academic, and research environments, with work spanning digital assistant development, enterprise system integration, teaching and learning pilots, and research computing support. Employing a balance of hands‑on development and engineering discipline the developer contributes to functional, reliable systems running in production.

Duties and Responsibilities:

• Delivers production-ready AI applications aligned with the institutional AI roadmap and defined delivery timelines
• Designs, builds, and maintains reusable AI components such as prompt libraries, agent templates, integration patterns, and API connectors
• Develops pilot AI solutions for teaching, learning, research, and administrative processes with defined success criteria and evaluation outputs
• Develops and maintains integrations between AI platforms and enterprise systems using RESTful APIs, event-driven services, and secure data exchange patterns
• Implements prompt workflows, agent logic, and automation scripts in collaboration with business analysts and technical leads
• Develops and supports protected computing environments for sensitive research workloads with attention to privacy, compliance, and secure operation
• Supports digital assistant and AI-enabled services through configuration, integration, performance monitoring, and ongoing operational support
• Creates and executes test plans covering model behaviour, safety constraints, output accuracy, and integration reliability
• Translates business and functional requirements into technical specifications, test criteria, and implementation plans
• Provides development effort estimates and assesses the impact of changes to AI workflows and integrations
• Contributes technical expertise to AI governance, risk assessment, compliance reviews, and supporting documentation
• Documents AI solutions, integrations, and workflows to support knowledge sharing and operational continuity
• Collaborates within the unit on infrastructure, system configuration, integrations, and operational support
• Communicates and acts in a respectful and professional manner, collaborates effectively, abides by policy, and contributes to a positive and productive work and learning environment
• Engages in continuous learning by participating in activities, training, and events related to Indigenous engagement, reconciliation, equity, diversity, and inclusion
• Demonstrates respect and dignity for all members of the community, actively contributes to an environment of acceptance and inclusion of others, and responds to different perspectives from a place of curiosity, respect, and non-judgement
• Undertakes related duties, as assigned, consistent with the nature of the position.

Qualifications:

• A bachelor’s degree in computer science, data science, engineering, or related field
• Minimum of four years of experience in applicant development, scripting, or integration within enterprise environments
• Experience working with AI and Machine Learning (ML) tools, cloud-based AI services, model orchestration platforms, or intelligent automation systems
• Experience with REST APIs, web services, JSON, secure data exchange patterns, and modern development workflows, including Git, CI/CD pipelines, and automated testing
• Knowledge of secure coding practices, authentication workflows, and data privacy considerations
• Effective communication and interpersonal skills
• Ability to work effectively both independently and collaboratively in a team
• Strong analytical, troubleshooting, and problem-solving abilities
• Ability to learn and apply emerging AI technologies, patterns, and tools
• Strong development skills in Python, Java, or JavaScript for AI integration, automation, or backend services is preferred
• Proficiency with multi-agent or generative AI frameworks, backend frameworks such as FastAPI, Flask, Django, and Spring Boot, identity or integration and portal systems, or responsible AI practices is an asset.