Senior Specialist, Context Engineer

Role Overview

We are seeking a Digital Supply Chain Senior Specialist Context Engineer to design, build, and govern the enterprise context layer (AI, Ontology Knowledge Graphs) that powers next-generation AI, analytics, and decision intelligence solutions.

This role sits at the intersection of Data Engineering, Ontology Design, Knowledge Graphs, Semantic Technologies, and Agentic AI. The successful candidate will establish the semantic foundation that enables AI systems to understand business concepts, relationships, processes, constraints, and enterprise knowledge.

As a Senior Specialist Context Engineer, you will transform disconnected data into contextual intelligence by creating ontologies, semantic models, knowledge graphs, metadata frameworks, and AI grounding capabilities that enable trusted and explainable AI across the organization.

What you will do in this role :

Context and Knowledge Architecture

• Design semantic models, ontologies, and taxonomies.
• Define business entities, concepts, relationships, and hierarchies.
• Establish enterprise-wide semantic standards and reusable patterns.
• Create contextual representations of business processes and decision flows.

Knowledge Graph Engineering

• Design and implement knowledge graphs.
• Model complex relationships across business domains.
• Develop graph schemas that support reasoning and contextual discovery.
• Enable relationship-based querying and inference.

AI Context Grounding

• Build context layers that improve AI accuracy and explainability.
• Develop GraphRAG solutions and semantic retrieval frameworks.
• Enable enterprise copilots and AI agents with trusted business context.
• Establish grounding mechanisms for LLMs and autonomous agents.
• Support memory and reasoning frameworks for AI systems.

Data Context Layer Development

• Create semantic mappings between business concepts and physical data.
• Integrate metadata, master data, business glossaries, and ontologies.
• Build contextual services that support decision intelligence solutions.
• Define semantic APIs and context-serving capabilities.

Business Collaboration

• Facilitate workshops with business stakeholders and SMEs.
• Capture business vocabulary, processes, and knowledge structures.
• Translate business understanding into machine-readable semantic models.
• Foster adoption of semantic and knowledge-driven practices.

Governance Stewardship

• Establish ontology governance processes.
• Define standards for semantic modeling and knowledge management.
• Manage lifecycle, quality, and stewardship of enterprise context assets.
• Develop best practices for AI-ready knowledge platforms.

General

• Throughout the process from ideation through deployment, the candidate will collaborate with design, business and technical team leaders to identify interdependencies, define, and document requirements thus facilitating robust product delivery.
• As a member of the Enterprise Orchestration, the Context Engineer establishes the technical capabilities working with various digital teams. Once the capability is established, they will help manage the backlog and guide the SteadyState team to deliver requirements thus enhancing the overall EO capabilities.
• Results-orientation with the proven ability and reputation for developing plans and implementing them successfully.
• Create documentation for the solutions you create, enhance and support in line with System Development Lifecycle company standards.
• Test IT solutions functionalities and analytical capabilities with the business use cases, train the users and document the results.
• Perform systems analysis and design on supply chain projects and programs
• Participate in evaluations and proof of concept activities.

What you should have:

• Bachelor's degree in business, information systems or related discipline.
• Experienced in using agile methods to bring new solutions through experimental/Proof of Concept (POC) stages and subsequent Minimum Viable Product (MVP), scale up and industrialization.
• Demonstrated ability to collaborate, communicate, supervise and coach across cultures, organizational levels and disciplines.
• Demonstrated success in building strong relationships across siloes.
• Proven leadership and people management skills are preferred, along with excellent written and verbal communication and presentation skills.
• Demonstrated ability to solve problems with a sense of ownership, enthusiasm and innovation.
• Ability to work in a global, multi-cultural, English-speaking environment.
• Ability to work both independently and collaboratively with a globally dispersed team
• Self-starter requiring minimal supervision. Demonstrated poise, confidence and professional maturity to interface with mid-level executives
• Ability to work with US and EU work hours with necessary overlap.
• 7+ years in Data Architecture, Information Architecture, Knowledge Engineering, or AI platforms.
• 3+ years building ontologies, semantic models, or knowledge graphs.
• Experience supporting enterprise AI initiatives.
• Experience working with cross-functional business and technical teams.

Technical Skills

• Semantic Technologies - Experience with One or more: RDF, RDFS, OWL, SKOS, SHACL, SPARQL
• Knowledge Graph Platforms - Experience with one or more: Neo4j, Amazon Neptune, Stardog, GraphDB, TigerGraph , Timbr.ai, Ontotext
• Data Metadata concepts - Data Modeling, Metadata Management, Business Glossaries, Master Data Management, Data Lineage, Data Governance
• AI Agentic Technologies - Generative AI, Large Language Models (LLMs), Retrieval Augmented Generation (RAG), GraphRAG, Vector Databases, Semantic Search, AI Agents, Agent Orchest

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.