Knowledge Engineer / Semantic Expert for AI

Accenture•St. Louis, MO

4d•$62,800 - $220,400

About The Position

Accenture’s Advanced Data & AI team helps organizations apply generative AI and semantic technologies to reinvent enterprise operations and business functions. The Knowledge Engineer formulates real-world business problems as practical, efficient, and scalable AI and Knowledge Graph problems, and guides a team in exploring and implementing methodologies, model-building techniques, algorithms, and architectures. The role calls for deep knowledge of knowledge engineering, generative AI, large language models, and multimodal models, with an emphasis on applying them to new business problems and use cases. The position involves designing, evaluating, and maintaining ontologies as needed, and demonstrating the business value of generative AI and Knowledge Graph approaches through appropriate methodologies and data architectures. The engineer collaborates with business and technical stakeholders—including users, use case representatives, business owners, engineers, architects, and UI designers—to deliver projects end to end. The role builds Knowledge Graph solutions that transform client data architecture and develops AI and semantic solutions. It also involves working with project teams and client stakeholders to develop graph-based Data & AI offerings, building client relationships, making the business case for semantic-layer solutions, and contributing to sales efforts when needed. The engineer is expected to continue developing current Data & AI capabilities, particularly agentic technologies, and provide thought leadership on trends, opportunities, innovations, limitations, and risks. Travel may be required from 0% to 100%, depending on business needs and client requirements. Compensation varies based on factors including office location, role, skills, and experience. The posting provides annual salary ranges for selected U.S. locations. Accenture offers medical, dental, vision, life, and long-term disability coverage, a 401(k) plan, bonus opportunities, paid holidays, and paid time off.

Requirements

Bachelor’s degree or equivalent, with a minimum of 12 years of work experience; alternatively, an Associate’s degree with a minimum of 6 years of work experience.
At least two of the listed experience areas: 2 or more years with Knowledge Graph technologies such as RDF, SPARQL, LPG, or SHACL.
At least two of the listed experience areas: 2 or more years with schema design, ontology management, and Knowledge Graph curation.
At least two of the listed experience areas: 2 or more years designing and developing Knowledge Graph solutions and graph-based machine-learning models, with functional and technical experience.
At least two of the listed experience areas: implementation of at least one end-to-end data pipeline for AI applications, particularly those involving LLMs or similar models, including hands-on design and configuration.
At least two of the listed experience areas: 2 or more years and strong knowledge of relational databases, object stores, graph databases, and vector databases.

Nice To Haves

2 or more years of hands-on experience with cloud platforms such as AWS, Azure, or GCP.
2 or more years of Python experience, including frameworks such as TensorFlow or PyTorch and ETL pipeline tools such as Apache NiFi or Airflow.
Practical experience with NLP and/or search techniques.
Experience with prompt engineering and LLMs for enterprise-scale applications.
Team lead experience.
Strong collaboration skills and ability to work across engineering, research, and product teams in multiple time zones.
External client-facing consulting experience.
Ph.D. in Computer Science, Computer or Electrical Engineering, Mathematics, or a related field.
Broad experience in diverse machine-learning techniques and agentic systems.

Responsibilities

Formulate real-world problems into practical, efficient, and scalable AI and Knowledge Graph problems.
Lead and guide a team in exploring and implementing methodologies, model-building techniques, algorithms, and appropriate architectures.
Design, evaluate, and maintain ontologies as required by the problem.
Develop methodologies and data architectures that demonstrate the business value of generative AI and Knowledge Graph approaches.
Collaborate with business and technical stakeholders to achieve end-to-end project development goals.
Build Knowledge Graph solutions that transform clients’ data architecture.
Design, develop, and implement AI and semantic solutions, ensuring the components work together seamlessly.
Work with project teams, team leaders, delivery leads, and client stakeholders to create graph-based Data & AI offerings.
Develop strong client relationships and gain the trust of key advisors.
Make the business case for recommended semantic-layer solutions.
Contribute to Accenture sales efforts when needed.
Continue learning and developing Data & AI solutions, especially agentic technologies, and provide thought leadership on trends, opportunities, innovations, limitations, and risks.