Knowledge Engineer

Overview

Design and structure knowledge frameworks that enable AI systems to reason and make informed decisions, leading a knowledge engineering scope within a large program.

What you'll do

• Capture and translate expert and unstructured knowledge into ontologies, knowledge graphs, and semantic models, ensuring accuracy and context for automation and insights.
• Apply advanced analytics on knowledge graphs to drive problem-solving and actionable insights.
• Lead the build of Knowledge Graph solutions that transform a client's data architecture within program scope.
• Direct the design, development, and implementation of AI and semantic solutions, ensuring all the pieces work together seamlessly.
• Design, evaluate, and maintain ontologies, and establish the standards others follow.
• Work with project leaders, delivery leads, and client stakeholders to create graph-powered Data & AI offerings.
• Make the business case for the semantic layer solution recommended to the client.
• Contribute to Accenture sales and pre-sales efforts.
• Provide thought leadership on technology trends, opportunities, innovations, and foreseeable limitations, risks, and concerns.
• Lead and manage the team, make team decisions, and engage with multiple teams on key decisions.
• Lead the development and refinement of knowledge representation models to enhance system reasoning capabilities.
• Facilitate knowledge sharing and alignment across cross-functional teams to ensure consistency and integration.
• Mentor junior team members to foster their professional growth.

What you'll need

• Bachelor's degree or equivalent, plus at least 4 of the following:
• Minimum 3 years of experience with Knowledge Graph technologies (RDF, SPARQL, LPG, SHACL).
• Minimum 3 years of experience in schema design, ontology management, and KG curation.
• Minimum 3 years of designing and developing KG solutions and graph-based ML models (functional + technical).
• Minimum 2 years of experience with end-to-end data pipeline implementations for AI applications (esp. LLMs), with hands-on design and configuration.
• Minimum 4 years of experience with strong knowledge of relational databases, object stores, graph databases (Stardog, Neo4J, Amazon Neptune), and vector databases.
• Minimum 2 years of experience in leading a team or workstream within a larger program.
• 2+ years of hands-on experience with cloud platforms (AWS, Azure, GCP).
• 2+ years of experience in Python, with experience in frameworks like Tensorflow, PyTorch, and tools for building ETL pipelines (e.g. Apache NiFi, Airflow).
• 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.
• Must have skills: Google Cloud Data Services.
• Minimum 7.5 years of experience is required.
• 15 years full time education.

Nice to have

• Microsoft Azure Data Services, Snowflake Data Warehouse.
• Ph.D. in Computer Science, Computer or Electrical Engineering, Mathematics, or a related field.
• Broad experience in diverse ML techniques and agentic systems.

Details

• Location: Bengaluru office.
• Full-time education requirement: 15 years.