Senior Knowledge Engineer

Accenture•Atlanta, GA

2d•Hybrid

About The Position

The Advanced Technology Centers (ATCs) are the engine for reinvention in our clients’ transformation journey. Powered by more than 255,000 people across 24 countries, ATCs provide our clients with seamless access to industry insights and innovative technology solutions. The ATCs make a tremendous impact in solving our clients’ business problems by leveraging innovation, intelligence, industry insights, new IT, and new technology skills. With the global environment changing at a faster pace, our clients are facing unprecedented challenges, and they need us more than ever before. As a Network, ATCs are positioned to unlock greater opportunities and exponential value for our clients. For our clients, the Network provides the strength of our geographic diversity, greater resilience, and seamless access to the deepest industry knowledge, the latest in Gen AI solutions, and tech expertise from around the world. For our people, it brings an opportunity to shape truly boundaryless career paths in a highly collaborative team of experts where they can learn from each other and solve the world’s most complex client challenges. You are a Knowledge Architect at the intersection of semantic AI and agentic systems — shaping the knowledge backbone of AI platforms by designing the ontologies, graphs, and data models that enable intelligent agents to reason, plan, and act. You are equally comfortable whiteboarding an ontology with a domain expert and pushing graph schemas to production alongside an ML team. You see the world as a graph, and you believe that well-structured knowledge is the foundation of truly intelligent machines. You thrive on translating complex, messy real-world knowledge into clean, reasoned, machine-readable structures that AI agents can act on — and you bring the rigor, curiosity, and collaboration to do it at scale. You will embed directly with clients as a trusted technology advisor and hands-on engineer — leading the architecture and development of knowledge graphs, ontologies, and semantic data models that power next-generation agentic AI systems at enterprise scale.

Requirements

Knowledge Representation & Ontology: Ontology design and engineering (OWL, RDF, RDFS), Taxonomy and thesaurus development, Semantic modeling and linked data principles, Schema design (schema.org, custom domain schemas) and W3C standards
Knowledge Graph Technologies: Property graph and RDF graph modeling, Graph databases: Neo4j, Amazon Neptune, TigerGraph, Stardog, SPARQL, Cypher, and Gremlin query languages, Graph traversal, reasoning, inference, entity resolution, and enrichment
Agentic AI & LLM Integration: Retrieval-Augmented Generation (RAG) architectures, LLM grounding and context design using structured knowledge, Agentic pipeline design: LangChain, LlamaIndex, AutoGen, Prompt engineering for knowledge-intensive, enterprise-scale applications, Neuro-symbolic AI concepts and reasoning frameworks
Data Modeling & Engineering: Conceptual, logical, and physical data modeling, Graph schema design and lifecycle management, Entity linking, disambiguation, and deduplication, Metadata management and data governance
Programming & Tooling: Python (primary); graph libraries: NetworkX, RDFLib, PyKEEN, SPARQL and graph query optimization, REST APIs, microservices integration, Git, CI/CD familiarity, Cloud platforms: AWS, Azure, GCP
Bachelor's degree or equivalent (minimum 12 years' work experience). Associate's degree requires minimum 6 years' equivalent work experience.
4+ years of experience in Knowledge Graph technologies (e.g., RDF, SPARQL, Gremlin, LPG, SHACL, RDFS)
2+ years of experience with schema design, ontology management, and Knowledge Graph curation
2+ years of experience in semantic modeling and linked data principles
2+ years of experience designing and developing knowledge graph solutions and graph-based machine learning models
2+ years of experience with relational databases, object stores, graph databases (e.g., Stardog, Neo4j, Amazon Neptune, TigerGraph), and vector databases
2+ years of experience in agentic pipeline design (LangChain, LlamaIndex, AutoGen)

Nice To Haves

2+ years of hands-on experience with cloud platforms (AWS, Azure, GCP)
2+ years of experience in Python, with frameworks like TensorFlow, PyTorch, and ETL pipeline tools (e.g., Apache NiFi, Airflow)
Practical experience with NLP and/or enterprise search techniques
Prompt engineering and LLM experience for enterprise-scale applications
Strong cross-functional collaboration skills across engineering, research, and product teams in multiple time zones
Ph.D. in Computer Science, Electrical Engineering, Mathematics, or a related field
Broad experience in diverse ML techniques and agentic systems

Responsibilities

Own the end-to-end design, governance, and maintenance of enterprise-scale knowledge graphs and ontologies, bridging structured domain knowledge with large-scale agentic AI pipelines to enable reasoning, planning, and decision-making.
Develop and govern ontologies, taxonomies, and semantic data models that formalize domain knowledge and support interoperability across systems and teams.
Define and enforce data modeling standards, schema design patterns, and best practices for structured and semi-structured knowledge representation.
Translate complex domain knowledge from subject matter experts into formal, machine-readable knowledge structures using RDF, OWL, SPARQL, or property graph models.
Lead knowledge engineering discovery workshops and working sessions with client stakeholders to surface, validate, and formalize domain knowledge requirements.
Collaborate with AI/ML engineers to integrate knowledge graphs as grounding and context layers for LLM-based agentic pipelines and retrieval-augmented generation (RAG) systems.
Design knowledge structures that support multi-step agent reasoning, tool use, and dynamic planning across heterogeneous data sources.
Work with project teams, team leaders, delivery leads, and client stakeholders to create standout Data & AI offerings powered by graph-based technologies.
Collaborate with data engineering and platform teams to build scalable pipelines for knowledge graph population, enrichment, and lifecycle management.
Develop strong client relationships and earn the trust of key stakeholders as a strategic advisor.
Communicate complex ontological concepts and graph architectures clearly to both technical and non-technical audiences.
Evaluate and pilot emerging tools, frameworks, and standards (e.g., LPG vs. RDF, Wikidata, schema.org, W3C standards).