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 : Data Engineering Good to have skills : Google Cloud Data Services Minimum 5 year(s) of experience is required Educational Qualification : 15 years full time education

Role Summary / Description

AI Powered Tech Talent

Engineer role in Knowledge Engineering for a strong individual contributor who helps build scalable Knowledge Graph, semantic layer, ontology, and AI-enabled data solutions using Google Cloud Platform (GCP)., the role works hands-on across the knowledge graph lifecycle from ingestion through modeling, curation, implementation, integration, and deployment. The role applies current methodologies, generative AI, LLM, multimodal, graph, search, and semantic techniques to practical business problems while collaborating with users, use case representatives, engineers, architects, UI designers, and delivery teams. The role must include industry experience or project exposure in domains such as banking, insurance, healthcare, retail, telecom, manufacturing, energy, public sector, or life sciences.

Key Responsibilities Build GCP-based knowledge engineering components using BigQuery, Cloud Storage, Dataflow, Dataproc, Pub/Sub, Vertex AI, Cloud Run, Cloud Functions, IAM, APIs, Cloud Logging, and monitoring services. Implement graph ingestion, ontology/schema pipelines, semantic data products, vector and search integrations, LLM grounding layers, and governed access patterns on GCP. Build Knowledge Graph components that contribute to transforming a client's data architecture. Design, develop, configure, test, and implement AI and semantic solutions that integrate clearly with the broader enterprise system. Work alongside project teams, delivery leads, engineers, architects, users, use case representatives, and UI designers to deliver assigned components of an end-to-end solution. Build solid working relationships with client counterparts on the workstream and communicate implementation progress, risks, dependencies, and technical findings clearly. Help assemble supporting evidence for recommended semantic layer solutions, including design rationale, implementation notes, validation inputs, data quality observations, and test outcomes. Support sales, solutioning, demos, accelerators, or reusable assets when called upon by providing technical inputs or implementation examples. Design, evaluate, maintain, and deploy ontologies, schemas, mappings, graph data models, metadata structures, validation rules, and knowledge graph components as needed. Keep developing skills in cutting-edge Data and AI solutions, especially agentic technologies, generative AI, LLMs, multimodal models, semantic search, graph RAG, and knowledge graph approaches. Share learnings with the team and help junior engineers understand practical development patterns, engineering standards, and knowledge graph implementation practices.