Knowledge Engineer Specialist

About the role

Accenture is helping companies use generative AI and semantic layer to reinvent their enterprise and optimize business functions for breakthrough innovation and competitive advantage. With over 1,600 professionals dedicated to generative AI, leveraging the depth and experience of more than 40,000 AI and data professionals across the company our Advanced Data & AI team brings together our Experienced Innovation, Strategic Investment, Exceptional Talent, and Power Ecosystem.

Responsibilities

• Formulate real-world problems into practical, efficient, and scalable AI and Knowledge Graph problems.
• Lead a team and provide guidance to explore and implement new methodologies, model building techniques, and cutting-edge algorithms, and apply these techniques with the right architecture to solve real-world problems.
• Have a deep understanding and ability to remain at the forefront of knowledge engineering, generative AI, LLM, and multi-modal models (driving innovation by applying these techniques to new business problems, use cases, and scenarios).
• Design, evaluate, and maintain ontologies as needed by the specific problem.
• Justify the value of innovative generative AI and knowledge graph approaches in the business problems, and construct methodologies and data architectures that clearly demonstrate their value.
• Work collaboratively with teams from both the business and technical side, including users, use case representatives, business owners, engineers, architects, and UI designers, to achieve end-to-end project development goals.

Requirements

• Bachelor's degree or equivalent (minimum 12 years) work experience. (If Associate’s Degree, must have minimum 6 years work experience)
• Minimum of 2 or more of the following:
• Minimum of 2 years of experience in Knowledge Graph technologies (e.g. RDF, SPARQL, LPG, SHACL)
• Minimum of 2 years of experience with schema design, ontology management, and Knowledge Graph curation
• Minimum of 2 years of experience in designing and developing knowledge graph solutions and graph-based machine learning models, functional and technical experience required
• Minimum of 1 end-to-end data pipeline implementation for AI applications, particularly those involving LLMs or similar models, including hands-on design and configuration
• Minimum of 2 years and strong knowledge of relational databases, object stores, graph databases (e.g. Stardog, Neo4J, Amazon Neptune), and vector databases

Qualifications

• Practical experience with NLP and/or Search techniques
• Prompt engineering, and LLMs for enterprise-scale applications
• Team lead experience
• Strong collaboration skills with the ability to work across engineering, research, and product teams across multiple time zones
• External client-facing consulting experience
• Broad experience in diverse ML techniques and agentic systems

Skills

• Hands-on experience with cloud platforms (AWS, Azure, GCP)
• Experience in Python, with experience in frameworks like Tensorflow, PyTorch, and tools for building ETL pipelines (e.g. Apache NiFi, Airflow)

Benefits

Not specified

Pay

Not specified

Schedule

Not specified

Location

• Houston, TX
• Albany, NY
• Arlington, VA
• Atlanta, GA
• Austin, TX
• Beaverton, OR
• Boston, MA
• Carmel, IN
• Charlotte, NC
• Chicago, IL
• Cincinnati, OH
• Cleveland, OH
• Columbus, OH
• Culver City, CA
• Denver, CO
• Detroit, MI
• Hartford, CT
• Irving, TX
• Miami, FL
• Milwaukee, WI
• Minneapolis, MN
• Morristown, NJ
• Mountain View, CA
• Nashville, TN
• New York City, NY
• Overland Park, KS
• Philadelphia, PA
• Pittsburgh, PA
• Raleigh, NC
• Sacramento, CA
• San Diego, CA
• San Francisco, CA
• Scottsdale, AZ
• Seattle, WA
• St. Louis, MO
• St. Petersburg, FL