Knowledge Engineering Semantic Expert

About AccentureAccenture helps the world's leading enterprises reinvent by building their digital core and unleashing the power of AI to create value at speed for organizations across industries. Our strategy is to be the reinvention partner of choice for our clients and lead in the safe, widespread adoption of AI, and to be the most client-focused, AI-enabled, great place to work in the world. We bring together the talent of our approximately 814,000 people with proprietary assets and platforms, deep process and industry expertise, and leading ecosystem relationships to deliver end-to-end solutions and measurable outcomes at scale. Through our Reinvention Services, we offer broad expertise across Cybersecurity, Digital Core, Finance, Industry and Enterprise, Song, Supply Chain and Engineering, and Talent, with advanced capabilities in AI and Data, Industry and Process, and Technology. We serve approximately 9,000 clients and generated approximately $74 billion in FY26 revenue. Visit us at accenture.com.

We Are:

Accenture is recognized as a global leader in AI and cloud transformation, helping businesses across industries reimagine the digital core for an AI-native operating model. Through partnerships with frontier AI labs such as Anthropic and OpenAI, and the leading cloud providers, Accenture delivers end-to-end services that drive innovation and business agility.

The Digital Core Reinvention practice builds industry super-ontologies: industry- and ecosystem-specific agentic AI and data engines that encode how a vertical actually operates — its entities, its regulations, its rhythms — so AI systems can reason inside the industry rather than about it.

You Are:

You are the technical authority responsible for turning semantic architectures and ontology models into robust, high-performance, and production-ready knowledge systems. You bridge the gap between semantic modeling, software engineering, and modern Big Data analytics infrastructure, building scalable graph data pipelines, microservices, and GraphRAG integration layers.

You stay hands-on in code: you write production ingestion jobs primarily in Python, tune complex graph queries,integrate graph databases (RDF Triple Stores / Property Graphs) with modern enterprise data lakes, and ensure high availability, low latency, and deterministic reasoning for downstream analytics and AI applications.

The Work:

• Architect, develop, and implement production knowledge graph solutions using RDF triple stores and/or property graph technologies.

• Translate semantic architectures, ontologies, and enterprise data models into scalable technical implementations.

• Build automated data ingestion and transformation pipelines using Python and PySpark to map relational, document, streaming, and unstructured data into graph representations.

• Design and implement identity resolution, entity disambiguation, entity linking, and link prediction capabilities to create unified, canonical graph records at enterprise scale.

• Integrate graph databases with enterprise data lakes, lakehouses, analytics platforms, APIs, and downstream applications.

• Develop high-throughput REST, GraphQL, and/or gRPC services that expose graph traversal, semantic search, and graph query capabilities.

Generative AI & GraphRAG

• Design and implement GraphRAG and hybrid vector-graph solutions that ground Large Language Models using contextual enterprise knowledge.

• Develop integration layers that connect knowledge graphs, vector stores, enterprise data platforms, and Generative AI applications.

• Build data processing and retrieval capabilities that enable LLMs and AI agents to leverage trusted, contextualized enterprise information.

• Partner with AI, data, ontology, and application engineering teams to operationalize knowledge-driven AI solutions.

Big Data & Production Engineering

• Leverage Databricks, Apache Spark, or comparable technologies to perform distributed graph transformations, large-scale entity resolution, and semantic data processing.

• Develop production-grade ETL/ELT pipelines supporting batch, streaming, structured, semi-structured, and unstructured data.

• Create automated testing, schema validation, and data-quality frameworks using technologies and approaches such as SHACL and Cypher constraints.

• Profile and optimize SPARQL, Cypher, and/or Gremlin query execution for high-volume and high-concurrency environments.

• Design indexing, caching, partitioning, and query optimization strategies to improve graph performance and scalability.

• Establish CI/CD practices for ontology and graph deployments, version control, automated testing, and infrastructure-as-code across cloud environments.

• Design solutions for high availability, low latency, scalability, observability, and production resiliency.

• Mentor senior and mid-level engineers in knowledge graph engineering, Python development, distributed data processing, and performance optimization.

Travel may be required for this role. The amount of travel will vary from 0 to 100% depending on business need and client requirements.

Here’s What You Need

• Minimum of 5 years of hands-on experience in Big Data engineering, data engineering, or analytics engineering, including development of scalable ETL/ELT pipelines for batch and/or streaming workloads.

• Minimum 2 years in Python, with hands-on experience using relevant libraries and frameworks such as PySpark, RDFLib, NetworkX, and FastAPI.

• Minimum 2 years experience with graph query and data languages such as SPARQL, Cypher, RDF, and/or SQL.

• 2 years knowledge graph and graph database technologies, including experience with platforms such as Neo4j, Stardog, GraphDB, and/or Amazon Neptune.

Bonus Points If You Have

• Hands-on experience with Databricks or comparable modern data platforms, including PySpark, Spark SQL, Delta Lake, Snowflake, Unity Catalog, and/or enterprise lakehouse architectures.

• Experience developing lakehouse-to-graph ingestion pipelines and large-scale semantic data transformation workloads.

• Experience implementing GraphRAG, Retrieval-Augmented Generation (RAG), vector search, and hybrid vector-graph architectures for Generative AI applications.

• Experience with SHACL, graph schema validation, ontology versioning, and semantic data-quality frameworks.

• Experience implementing RDF and/or property graph architectures in production environments.

• Experience with regulatory or compliance-driven modeling.

• Graph-based ML alongside symbolic modeling.

Compensation at Accenture varies depending on a wide array of factors, which may include but are not limited to the specific office location, role, skill set, and level of experience. As required by local law, Accenture provides a reasonable range of compensation for roles that may be hired as set forth below.We anticipate this job posting will be posted until 12/04/2026.Accenture offers a market competitive suite of benefits including medical, dental, vision, life, and long-term disability coverage, a 401(k) plan, bonus opportunities, paid holidays, and paid time off. See more information on our benefits here:

U.S. Employee Benefits | Accenture (https://www.accenture.com/us-en/careers/local/benefits)

Role Location Annual Salary RangeCalifornia $94,400 to $293,800Colorado $94,400 to $253,800Connecticut $94,400 to $253,800District of Columbia $100,500 to $270,300Illinois $87,400 to $253,800Maine $80,400 to $216,200Maryland $94,400 to $253,800Massachusetts $94,400 to $270,300Minnesota $94,400 to $253,800New York $87,400 to $293,800New Jersey $100,500 to $293,800Ohio $87,400 to $235,000Virginia $87,400 to $270,300Washington $100,500 to $270,300

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