Forward Deployment Engineer – Enterprise Knowledge Systems

In this role, you will help customers build intelligent enterprise knowledge ecosystems to power AI applications. You will work with clients to assess knowledge landscapes and design production-ready solutions using knowledge graphs, semantic technologies, and contextual data platforms. You’ll craft scalable architectures and PoVs that enable AI-native transformation, with a focus on explainability, security, and governance. This is an opportunity to shape how enterprises leverage knowledge-powered AI at scale within a collaborative, diverse environment.

• Engage with customers to identify business challenges solvable by contextual AI and knowledge-driven architectures
• Assess enterprise knowledge assets, data, taxonomies, metadata, and knowledge management capabilities
• Define roadmaps for enterprise knowledge platforms enabling AI-native transformation
• Design, develop, and deploy production-ready knowledge systems and rapid PoVs
• Architect solutions using knowledge graphs, semantic layers, ontologies, metadata management, and contextual data platforms
• Design GraphRAG, Hybrid RAG, Agentic RAG, and contextual retrieval architectures for better AI reasoning and explainability
• Develop scalable ontologies, taxonomies, semantic models, and knowledge representations
• Integrate knowledge platforms with AI agents, LLMs, enterprise apps, and workflows
• Evaluate emerging technologies and recommend architectures and engineering approaches
• Ensure solutions are secure by design, scalable, maintainable, and governance-aligned

• 5+ years designing, building, and maintaining production-grade enterprise knowledge systems focusing on knowledge graphs, ontologies, and semantic technologies
• Proven experience delivering scalable and maintainable knowledge graph solutions for enterprise applications
• Expertise in ontology engineering, semantic modeling, taxonomy design, and enterprise knowledge representation
• Deep understanding of graph databases, RDF, OWL, SPARQL, property graphs, linked data, and semantic interoperability
• Experience designing and implementing GraphRAG, Hybrid RAG, contextual retrieval, vector search, semantic search, and enterprise knowledge retrieval architectures
• Experience integrating knowledge systems with LLMs, AI agents, ML, generative AI, and enterprise applications
• Broad knowledge of modern AI, cloud, and enterprise technologies with ability to learn and adopt emerging tech
• Experience with containerization, CI/CD, MLOps/LLMOps, observability, and secure-by-design engineering practices
• Demonstrated ability to leverage AI-assisted development and intelligent engineering tools to accelerate delivery
• Excellent consulting, communication, stakeholder management, and problem-solving skills

• consulting
• communication
• stakeholder management
• knowledge graphs
• ontology engineering
• semantic modeling