Staff AI Engineer, Data Ontologist
TELUS Digital
full-time
Posted on:
Location Type: Remote
Location: Brazil
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Job Level
About the role
- 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.
Requirements
- 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 construction, including latency vs. completeness, precision vs. recall, and static vs. dynamic context.
- Experience working in cloud environments (AWS, Azure, GCP) and integrating AI systems into production-grade architectures.
- Ability to communicate complex semantic and AI concepts clearly across technical and business stakeholders.
Benefits
- Health and dental plan
- Life insurance
- Monthly voucher for meals, culture, education, health and mobility
- Child care assistance and more!
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
semantic systems designontologiesknowledge graphsknowledge representation techniquestaxonomy designentity-relationship modelingcontext engineeringretrieval-augmented generationembeddingscontext pipeline design
Soft Skills
communicationcollaborationproblem-solvingadaptabilitystakeholder engagement