AI Engineer - Data Ontologist

Who We Are

Welcome to TELUS Digital— where innovation drives impact at a global scale. As an award-winning digital product consultancy and the digital division of TELUS, one of Canada’s largest telecommunications providers, we design and deliver transformative customer experiences through cutting‑edge technology, agile thinking, and a people‑first culture.

With a global team across North America, South America, Central America, Europe, Africa, and APAC, we offer end‑to‑end expertise across various service offerings:
Web, Mobile & Digital Marketing | Enterprise AI | Customer Care AI & Technology | Enterprise Technology Modernization

From mobile apps and websites to voice UI, chatbots, AI, customer service, and in‑store solutions, TELUS Digital enables seamless, trusted, and digitally powered experiences that meet customers wherever they are — all backed by the secure infrastructure and scale of our multi‑billion‑dollar parent company.

US Hybrid:

Our AI Engineers are integral parts of our Data & AI team at TELUS Digital. To help retain our deep culture of collaboration, this role will maintain an in‑office presence in a hybrid capacity (Tuesdays, Wednesdays, Thursdays). This role may be located in Boston, MA, Columbus, OH, Charlottesville, VA, or Durham, NC.

As a AI Engineer - Data Ontologist, you will design and operationalize the semantic and contextual foundations that power intelligent AI systems. This role is critical in enabling AI agents to reason, retrieve, and act effectively by structuring knowledge, defining relationships, and optimizing how context is constructed, retrieved, and used in real time.

You will bridge data, AI, and application layers, transforming fragmented enterprise information into coherent, machine‑interpretable context that drives high‑performance agentic systems and decision‑making.

• Design and implement context architectures that enable AI systems to access, interpret, and reason over enterprise knowledge.
• Develop and maintain ontologies, schemas, and knowledge representations that structure domain knowledge across systems, ensuring consistency, reusability, and scalability.
• Define and optimize context assembly pipelines, including retrieval strategies, ranking logic, memory handling, and prompt/context composition for LLM‑based systems.
• Build and manage semantic layers over structured and unstructured data, enabling effective grounding of AI agents in real‑world business context.
• Design and implement knowledge graphs and context graphs to model relationships between entities, actions, and outcomes across enterprise systems.
• Collaborate with AI Engineers and Data teams to align embeddings, chunking strategies, and vector storage with ontology and semantic design.
• Establish standards for context quality, including evaluation frameworks for relevance, coherence, completeness, and business impact.
• Enable interoperability across AI systems by defining shared context interfaces, schemas, and protocols (e.g., MCP or API‑based context services).
• Continuously refine context systems based on agent performance, feedback loops, and operational insights.
• Translate complex semantic and contextual concepts into actionable implementations for both technical and non‑technical stakeholders.

• Strong experience in designing semantic systems, ontologies, or knowledge graphs within complex data environments.
• Hands‑on experience with knowledge representation techniques, including taxonomy design, entity‑relationship modeling, and graph‑based structures.
• Experience working with LLM‑based systems, particularly in context engineering, retrieval‑augmented generation (RAG), or agentic AI architectures.
• Deep understanding of embeddings, vector databases, and retrieval strategies, and how they interact with structured semantic layers.
• Experience designing context pipelines that integrate multiple data sources (APIs, databases, documents) into coherent inputs for AI systems.
• Familiarity with frameworks and tools related to graph databases (e.g., Neo4j), semantic layers, or metadata management.
• Strong understanding of trade‑offs in context…