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Compensation Range

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Posting End Date

Note: Applications will be accepted until 11:59 PM on the Posting End Date.

Job End Date

At UBC, we believe that attracting and sustaining a diverse workforce is key to the successful pursuit of excellence in research, innovation, and learning for all faculty, staff and students. Our commitment to employment equity helps achieve inclusion and fairness, brings rich diversity to UBC as a workplace, and creates the necessary conditions for a rewarding career. 

Job Summary

The AI Developer is responsible for supporting the need for innovation in service delivery for administrative units at UBC through the rapid prototyping, experimenting with, and validating of AI tools and services to explore their potential value for UBC based on identified business challenges. This role is focused on assessing AI technologies, testing small-scale AI solutions, and iterating based on feedback to determine feasibility before scaling the tools for broader adoption. The Sr. AI Developer will explore AI capabilities in a controlled, low-risk environment and identify opportunities for innovation.

The successful candidate will work with a variety of AI and machine learning frameworks, including open-source libraries (e.g., Hugging Face, LangChain, spaCy), cloud AI platforms (e.g., AWS SageMaker, Amazon Bedrock, OpenAI, Azure AI), and other emerging tools. This role will involve designing proof-of-concept solutions, working with stakeholders to refine AI applications, and experimenting with new AI models and methodologies to assess their feasibility for UBC’s administrative and operational needs.

Organizational Status

The Digital Experience Lab (DxL) under the umbrella of UBC Office of the CIO (OCIO) has been established to help the IT organization safely experiment with artificial intelligence (AI) by testing and proving concepts with the intention to scale validated initiatives into future IT services. The goal is to explore AI capabilities through agile experimentation, shaping concepts before transforming them into enhanced services for the UBC community.

The DxL is focused on incorporating AI and Machine Learning (ML) capabilities to address administrative challenges, improve operational efficiencies, and evaluate AI’s potential impact before scaling solutions to facilitate broader institutional adoption.

This position reports to the Sr. Manager, Digital Experience and Creative Media and works in collaboration with management and staff across various portfolios within the Office of the CIO and IT service teams. The role involves close interaction with business stakeholders, and other organizational units across UBC to ensure AI solutions are usable, practical, and aligned with UBC’s strategic objectives.

Work Performed

AI Prototyping & Experimentation:

• Develop and test proof-of-concept (PoC) AI applications to explore emerging AI capabilities.
• Assess and experiment with new AI tools, models, and platforms to determine their viability for UBC’s administrative and operational needs in response to documented business challenges.
• Quickly iterate on AI solutions based on stakeholder feedback, ensuring alignment with real-world use cases.

AI Model Development & Engineering:

• Design and implement machine learning models for specific business challenges.
• Optimize AI models through lightweight experimentation, rapid evaluation, and iterative development
• Utilize low-code and no-code AI platforms where applicable to accelerate prototyping.

Chatbot & AI Tool Development:

• Build and refine intelligent AI-driven assistants using techniques such as:
• Retrieval-Augmented Generation (RAG) for improved response accuracy.
• Prompt Engineering to enhance model interaction and engagement.
• Knowledge Graph Integration for structuring complex data relationships.
• Semantic Search & Contextual Retrieval using vector-based search for knowledge discovery.

Data Processing & Feature Engineering:

• Leverage AI to extract and process unstructured data (PDFs, text, images) to support innovation use cases.
• Implement AI-driven techniques for data enrichment and pattern recognition to uncover insights, ensuring appropriate privacy and security policies and guidelines are followed.
• Explore novel AI-driven feature engineering techniques that improve model interpretability and usability.

AI Governance, Ethics, & Responsible AI:

• Collaborate with governance teams to ensure ethical AI practices, transparency, and compliance.
• Participate in the development of responsible AI guidelines for AI experimentation at UBC.
• Work with stakeholders to address privacy, security, and ethical risks in AI applications.

Collaboration & Knowledge Sharing:

• Work closely with functional and  IT teams to co-develop AI solutions.
• Engage with end users, conduct usability testing, and refine AI interactions based on feedback.
• Document solutions and support transition to operations
• Share lessons learned from AI experiments and share insights within the UBC IT community.

Continuous Learning & AI Trend Analysis:

• Monitor emerging trends in AI and machine learning, assessing their relevance to UBC.
• Support internal workshops and knowledge-sharing sessions to build AI literacy across teams.
• Explore the potential of generative AI, autonomous agents, and multimodal AI applications for future-proofing UBC’s AI capabilities to support administrative use cases. 

Consequence of Error/Judgement

This position plays a critical role in supporting the development of AI solutions to support the efficient and effective delivery of administrative services at UBC, and de-risking AI adoption by ensuring the rigorous testing and evaluating AI solutions before they are operationalized at scale. Errors in judgment, lack of experimentation rigor, or premature deployment of unproven solutions could lead to:

• Misalignment between AI implementations and actual business needs.
• Unnecessary expenditure on AI solutions that do not deliver expected value.
• Ethical or governance concerns if AI models are not properly vetted before broader use.
• Missed opportunities to innovate due to a failure to assess cutting-edge AI advancements.
• Loss of trust in the capability for UBC to deploy AI tools responsibly

Supervision Received

• Works under the administrative direction of the Senior Manager, Digital Experience and Creative Media and collaborates with functional lead, project managers, and technical teams.
• Works independently to explore AI solutions, while also integrating insights from cross-functional teams.

Supervision Given

Provides mentorship and technical guidance to junior AI developers, helping them develop AI prototypes effectively.

Minimum Qualifications

Undergraduate degree in a relevant discipline. In-depth knowledge of applications and the business requirements supporting them. Minimum of five years of related experience, or the equivalent combination of education and experience.

- Willingness to respect diverse perspectives, including perspectives in conflict with one’s own.

- Demonstrates a commitment to enhancing one’s own awareness, knowledge, and skills related to equity, diversity, and inclusion.

Preferred Qualifications

• Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or a related field.
• Experience in AI experimentation, machine learning development, or software prototyping.
• Prior work in research-oriented AI projects, UX-focused AI development, or experimental AI applications.
• Strong understanding of ML models, generative AI, and supervised learning.
• Strong communication skills, with the ability to explain AI concepts to non-technical stakeholders.
• Hands-on experience with rapid AI prototyping, chatbot development, and NLP frameworks.
• Familiarity with vector databases, semantic search, and agent-based AI solutions.
• Experience working with cloud-based AI platforms such as AWS SageMaker, Azure OpenAI, OpenAI APIs, and Google AI.
• Proven experience in data cleaning, feature engineering, and exploratory data analysis (EDA) on real-world, messy datasets.
• Hands-on experience training, evaluating, and tuning machine learning models beyond API-level usage.
• Familiarity with MLOps practices including model versioning, experiment tracking, monitoring, and retraining strategies.
• Experience with model evaluation, interpretability, and performance trade-off analysis (e.g., explain ability techniques, drift detection).
• Experience in AI trend analysis and technology assessment.
• Background in responsible AI, fairness, and bias mitigation in AI models.