Knowledge Engineer

Project Role : Knowledge Engineer Project Role Description : Design and structure knowledge frameworks that enable AI systems to reason and make informed decisions. 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. Must have skills : AWS BigData Good to have skills : Graph Databases, Neo4j, Data Engineering Minimum 7.5 year(s) of experience is required Educational Qualification : 15 years full time education Role Summary / Description AI Powered Tech Talent Senior Engineer role in Knowledge Engineering focused on designing, building, and leading significant workstreams for enterprise-scale Knowledge Graph, semantic layer, ontology, and AI knowledge solutions on Amazon Web Services (AWS). In this role you will lead a substantial knowledge engineering scope within a large program, translates real-world business problems into scalable AI and KG solutions, guides technical direction, and contributes to thought leadership, reusable assets, and delivery standards. The role must bring relevant industry experience across domains such as BFSI, healthcare, retail, telecom, manufacturing, energy, public sector, or life sciences, applying semantic AI, knowledge graphs, LLM grounding, data pipelines, and cloud-native engineering patterns to deliver measurable client value.

Engineer AWS-based knowledge engineering solutions using services such as Amazon Neptune, S3, Glue, Lambda, EMR, Redshift, Athena, OpenSearch, Bedrock, SageMaker, IAM, Step Functions, APIs, and monitoring/security services. Build graph ingestion pipelines, semantic data products, vector integrations, LLM grounding layers, APIs, and governed access patterns on AWS while improving performance, reliability, scalability, and cost efficiency. Lead the build of Knowledge Graph solutions that transform client data architecture within a large program scope. Direct the design, development, and implementation of AI, semantic layer, ontology, taxonomy, schema, graph modeling, and knowledge curation solutions, ensuring all components work together seamlessly. Work with project leaders, delivery leads, client stakeholders, architects, data engineers, AI engineers, product teams, and domain SMEs to create standout graph-powered data and AI offerings. Develop strong client relationships, earn trust as a key advisor, and explain the business value of semantic layer, ontology, and knowledge graph solutions. Make the business case for the recommended semantic layer solution and contribute meaningfully to sales, pre-sales, estimation, proposal inputs, and solution shaping activities. Provide thought leadership on technology trends, innovation opportunities, limitations, risks, delivery concerns, and practical adoption of knowledge graphs, semantic AI, LLM grounding, RAG, and agentic systems. Design, evaluate, and maintain ontologies, schemas, standards, and reusable engineering patterns, while guiding teams on data model quality and implementation methods. Lead a team or workstream within a larger program, mentor engineers, review design/code/configuration, and guide adoption of new methodologies, model building techniques, and algorithms. Collaborate across business and technical teams to drive end-to-end delivery for the assigned scope and demonstrate value to business and technology stakeholders.

Bachelor's degree or equivalent in Computer Science, Information Technology, Engineering, Mathematics, Data Science, or a related field. Minimum 3 years of experience with Knowledge Graph technologies such as RDF, SPARQL, LPG, SHACL, OWL, graph query languages, schema design, ontology management, and KG curation. Minimum 3 years of experience in schema design, ontology management, semantic modeling, taxonomy management, metadata