Agentic AI - Knowledge Graph Engineer (100% REMOTE)

Job Title - Agentic AI Engineer Duration - Contract-To-Hire Location - 100% Remote Year of experience - 2 to 5 years only Customer - Our customer is a stealth mode start-up. Why Join Us • We are a startup developing a general purpose diagnostics engine • Work on cutting-edge problems at the intersection of AI reasoning model, knowledge representation, and software architecture. • Collaborate with world-class engineers, researchers, and domain experts. • Competitive compensation, equity, and benefits Position Overview We are seeking a Junior Agentic AI - Knowledge Graph Engineer to lead the design, development, and deployment of enterprise-scale Knowledge Graphs and AI reasoning systems. The ideal candidate will have deep expertise in graph-based data models, semantic technologies, and AI architectures — including reasoning engines, agents, and multimodal AI systems. You will work cross-functionally with data scientists, ML engineers, software developers, and domain experts to create intelligent systems capable of representing, reasoning, and learning from structured and unstructured data. Key Responsibilities • Architect and design scalable and maintainable Knowledge Graphs using technologies such as RDF, OWL, SHACL, SPARQL, Neo4j, and GraphQL. • Lead efforts to integrate AI models with Knowledge Graphs to enable inference, logical reasoning, and multi-hop question answering. • Define and implement semantic enrichment pipelines using NLP, entity linking, relationship extraction, and ontology alignment. • Collaborate on the development of intelligent agents and autonomous tools that utilize symbolic and sub-symbolic reasoning. • Guide and mentor engineering teams on best practices for building and using graph-based AI systems. • Partner with data engineering and platform teams to manage large-scale graph data infrastructure (e.g., AWS Neptune, Blazegraph, TigerGraph). • Evaluate and implement reasoning engines, including rule-based systems, theorem provers, or differentiable logic layers. • Prototype and productionize solutions that combine LLMs with structured knowledge for real-time decision support, semantic search, or recommendation systems. • Author technical documentation, whitepapers, and reusable architectural blueprints. Required Qualifications • Experience in software engineering, with focused on Knowledge Graphs or semantic technologies and using AI models. • Strong experience with RDF, OWL, SPARQL, and related semantic web standards. • Proficiency in graph databases such as Neo4j, Amazon Neptune, ArangoDB, or equivalent. • Deep understanding of AI reasoning systems (symbolic AI, rule-based systems, hybrid AI, neuro-symbolic architectures). • Experience integrating Large Language Models (LLMs) and knowledge graphs using frameworks like LangChain, CrewAI, or similar. • Familiarity with knowledge representation, ontologies, and taxonomy design. • Hands-on experience with Python (preferred), Java, or other modern programming languages. • Exposure to agent-based systems, planning algorithms, or multi-agent frameworks. • Strong communication skills and ability to lead cross-disciplinary technical discussions. Preferred Qualifications • Advanced degree (MS or PhD) in Computer Science, AI, Knowledge Representation, or related field. • Experience with reasoning engines such as Drools, Prolog, OpenCyc, RDFox, or LogicBlox. • Experience building AI tools or agents that interact with APIs or tools autonomously. • Publications or open-source contributions in semantic technologies, AI reasoning, or graph AI.