Ontologist, Agentic Platform Architect

Kinaxis Inc.•Remote,

10d•Hybrid

About The Position

The AI team is responsible for advancing machine learning solutions in the supply and demand space across industries such as Retail, Consumer Packaged Goods, and Life Sciences. Our work spans forecasting, optimization, replenishment, recommendation, explainability, and emerging AI techniques that help customers solve complex, real-world planning challenges. What makes this team unique is that we operate at the intersection of applied research, product innovation, and customer impact. We explore new methods, develop novel approaches, and turn them into practical capabilities that can shape the future of the Kinaxis platform. This is a team for people who want to work on meaningful problems, push the boundaries of applied AI in real business settings, and see their ideas influence products used by customers around the world.

Requirements

Masters or PhD in Computer Science, Artificial Intelligence or a related field.
Relevant experience in enterprise software architecture, applied AI, data modeling, knowledge graph or semantic systems, or supply chain technology.
Deep practical understanding of enterprise supply chain systems or adjacent operational systems, including what data exists in those systems, how it is modeled, how to connect to it, and how it can be used to solve real business problems.
Strong hands-on experience building designing platform-level software, developer tooling, composable capabilities, workbench-style products, prototypes, proof-of-concepts, or early systems that demonstrate the value of structured knowledge and agentic AI.
Strong data modeling, semantic modeling, ontology, knowledge graph, or knowledge representation experience, with the ability to apply these skills pragmatically to supply chain and enterprise platform problems.
Experience defining reusable architecture patterns, data model governance, semantic or ontology standards, versioning, lifecycle management, and alignment across enterprise domains.
Ability to define long-term evolution strategies for agentic enterprise platforms, balancing speed of delivery with durable, scalable, and extensible design.
Experience applying emerging techniques in agentic AI, knowledge representation, semantic systems, or enterprise data platforms, with a track record of translating new concepts into practical, product-oriented solutions.
Hands-on experience with knowledge graph, data, and integration platforms and pipelines, including design, ingestion, transformation, entity resolution, schema or ontology alignment, validation, update patterns, and performance at scale.
Hands-on agentic AI and agentic engineering experience is required, including building software artifacts, applications, plans, evaluators, or workflows using agentic development approaches and GitHub-native engineering practices.
Strong technical judgment and the ability to evaluate emerging semantic, graph, agentic AI, enterprise integration, and platform technologies.
Demonstrated ability to influence technical direction across product, engineering, platform, and customer-facing domains through expertise, credibility, and collaboration.
Excellent communication skills, with the ability to bring clarity and alignment to complex concepts across AI, supply chain systems, enterprise data, platform architecture, and product strategy.
Ability to distinguish platform architecture from customer-specific implementation, building reusable machinery that downstream teams can configure and extend.

Nice To Haves

Experience with semantic technologies such as RDF, OWL, SHACL, or SPARQL.
Background in temporal modeling, digital twins, operational intelligence systems, or enterprise orchestration platforms.
Experience contributing to research, standards, open-source projects, or innovation in semantic systems, applied AI, agentic AI, or enterprise data platforms.
Experience with enterprise SaaS products and operating AI, graph, data, or agentic platforms at scale.
Experience with RAG, LLM applications, explainable AI, evaluators, or agentic quality frameworks.

Responsibilities

Provide hands-on technical leadership in agentic AI, knowledge graph, semantic, and data modeling architecture while designing platform-level tooling that can connect Maestro and planning data with broader enterprise systems such as warehouse management, inventory, Salesforce, and partner agentic environments or similar ecosystems.
Design the underlying data, graph, and integration architecture, including ingestion pipelines, transformation and mapping, entity resolution, schema alignment, validation, batch and streaming updates, and patterns that allow agents to traverse enterprise data and graph structures.
Contribute to key technical decisions across platform architecture, graph architecture, data modeling, AI integration, agentic workflows, quality evaluators, constraint validation, and query-time reasoning at scale.
Mentor others and help build a culture of structured thinking, semantic clarity, pragmatic platform architecture, agentic engineering, and product-oriented innovation.
Architect large-scale graph, semantic, and data platforms that integrate structured, semi-structured, and unstructured data across planning, warehouse, inventory, and other enterprise systems.
Drives the technical evaluation of emerging graph, semantic, agentic AI, and enterprise data technologies with clear, defensible trade-off analysis.
Demonstrated ability to identify, evaluate, and apply emerging research and technologies in agentic AI, knowledge representation, semantic systems, and enterprise platforms, translating them into scalable architecture patterns and product capabilities that enable reuse, composability, and extensibility across products.
Ability to define quality architecture for agentic systems, including evaluators, validation patterns, constraints, guardrails, and approaches for building high-quality software in an agentic technology environment.