Knowledge Engineer / Semantic Expert for AI

Accenture•Mountain View, CA

About The Position

Accenture is hiring a Knowledge Engineer to help clients apply generative AI and semantic technologies to transform enterprise operations and business functions. The role formulates business problems as practical, efficient, scalable AI and Knowledge Graph solutions, and leads and guides teams in exploring and implementing methodologies, model-building techniques, algorithms, and architectures. The position requires expertise in knowledge engineering, generative AI, large language models, and multimodal models, with an emphasis on applying them innovatively to business use cases. Depending on the problem, the Knowledge Engineer designs, evaluates, and maintains ontologies. The role also develops methodologies and data architectures that demonstrate the business value of generative AI and Knowledge Graph approaches, and collaborates with business and technical stakeholders to deliver projects end to end. The Knowledge Engineer builds Knowledge Graph solutions and designs, develops, and implements AI and semantic solutions. They work with project teams and client stakeholders to develop graph-based Data & AI offerings, establish trusted client relationships, explain the business case for recommended semantic-layer solutions, and contribute to sales efforts as needed. The position also involves ongoing learning and technology leadership, including advising on trends, opportunities, innovations, limitations, risks, and concerns, particularly related to agentic technologies. Travel may range from 0% to 100%, depending on business needs and client requirements.

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 2 years of experience in Knowledge Graph technologies such as RDF, SPARQL, LPG, or SHACL.
At least 2 years of experience with schema design, ontology management, and Knowledge Graph curation.
At least 2 years of experience designing and developing Knowledge Graph solutions and graph-based machine learning models, including functional and technical experience.
At least one end-to-end data pipeline implementation for AI applications, particularly those involving LLMs or similar models, including hands-on design and configuration.
At least 2 years of strong knowledge of relational databases, object stores, graph databases such as Stardog, Neo4J, or Amazon Neptune, and vector databases.
Applicants for employment in the United States must have work authorization that does not now or in the future require visa sponsorship.

Nice To Haves

At least 2 years of hands-on experience with cloud platforms such as AWS, Azure, or GCP.
At least 2 years of experience with Python, 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 with diverse machine learning techniques and agentic systems.

Responsibilities

Formulate real-world business problems as practical, efficient, and scalable AI and Knowledge Graph problems.
Lead and guide a team in exploring and implementing methodologies, model-building techniques, algorithms, and architectures.
Design, evaluate, and maintain ontologies as needed for specific problems.
Construct methodologies and data architectures that demonstrate the business value of generative AI and Knowledge Graph approaches.
Collaborate with business and technical stakeholders—including users, use case representatives, business owners, engineers, architects, and UI designers—to achieve end-to-end project goals.
Build Knowledge Graph solutions that transform clients’ data architecture.
Design, develop, and implement AI and semantic solutions, ensuring their components work together seamlessly.
Work with project teams, team leaders, project delivery leads, and client stakeholders to create graph-based Data & AI offerings.
Develop strong client relationships and earn the trust of key advisors.
Make the business case for recommended semantic-layer solutions.
Contribute to Accenture sales efforts as needed.
Continue learning and developing Data & AI solutions, especially agentic technologies.
Provide thought leadership on technology trends, opportunities, innovations, and foreseeable limitations, risks, and concerns.
Travel as required by business needs and client requirements; travel may range from 0% to 100%.