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 : Google Cloud Data Services

Good to have skills : Graph Databases, Neo4j, Data Engineering

Minimum 15 Year(s) Of Experience Is Required

Educational Qualification : 15 years full time education

Role Summary / Description

AI Powered Tech Talent

Technical Architect role in Knowledge Engineering focused on leading enterprise-scale knowledge graph, semantic layer, ontology, and AI knowledge architecture solutions on Google Cloud Platform (GCP). The role owns the complete knowledge engineering scope for strategic and complex programs, sets architecture standards, guides teams, shapes client solutions, and translates real-world business problems into scalable AI and knowledge graph solutions. The role must bring industry experience across domains such as BFSI, healthcare, retail, telecom, manufacturing, energy, public sector, or life sciences, applying semantic AI, knowledge graphs, LLM grounding, governed data products, and cloud-native architecture patterns to create reusable assets and measurable business value.

Key Responsibilities

Define GCP reference architectures for knowledge graph, semantic layer, and AI knowledge applications using services such as BigQuery, Cloud Storage, Dataflow, Dataproc, Vertex AI, Cloud Run, Pub/Sub, IAM, Cloud Logging/Monitoring, and graph/vector ecosystem services.

Architect cloud-native data pipelines, graph ingestion, ontology management, vector/database integrations, API layers, LLM grounding patterns, and governed knowledge access models on GCP.

Lead the complete Knowledge Graph and Knowledge Engineering solution scope that transforms data architecture for strategic, complex client programs.

Own the design, development, and implementation of AI, semantic layer, ontology, taxonomy, schema, graph modeling, and knowledge curation solutions across the program.

Partner with project leaders, delivery leads, senior client stakeholders, architects, product teams, data engineers, AI engineers, and domain SMEs to create standout graph-powered offerings.

Develop trusted-advisor relationships with senior client stakeholders and make a clear business case for semantic layer, knowledge graph, and enterprise AI knowledge architecture solutions.

Lead architecture governance, design reviews, solution estimation, pre-sales support, proposal inputs, implementation planning, and technical risk management for complex knowledge engineering programs.

Set standards for ontology design, semantic modeling, metadata management, data governance, lineage, knowledge graph curation, and reusable engineering patterns across programs.

Build and mentor multidisciplinary teams, establish capability development plans, and guide delivery quality for knowledge engineers, data engineers, AI engineers, and platform specialists.

Drive thought leadership, innovation, reusable assets, accelerators, and modern methods around knowledge graphs, semantic AI, LLM grounding, RAG, agentic systems, and graph-based AI patterns.

Translate industry-specific business problems into scalable knowledge-driven architectures and reusable assets that advance the discipline beyond a single engagement.

Required Qualifications

Bachelor's degree or equivalent in Computer Science, Information Technology, Engineering, Mathematics, Data Science, or a related field.

Minimum 6 years of experience with Knowledge Graph technologies such as RDF, SPARQL, LPG, SHACL, OWL, schema design, ontology management, and knowledge graph curation.

Minimum 6 years of experience in schema design, ontology management, semantic modeling, taxonomy management, metadata management, and knowledge graph curation.

Minimum 4 years of experience designing and developing Knowledge Graph solutions and graph-based ML models across functional and technical workstreams.

Minimum 3 years of experience implementing end-to-end data pipelines for AI applications, especially LLM-enabled or enterprise knowledge applications, with hands-on design and configuration.

Minimum 6 years of experience with relational databases, object stores, graph databases such as Stardog, Neo4j, Amazon Neptune or equivalent, and vector databases.

Minimum 6 years of managerial or technical leadership experience leading teams and explaining the value of semantic layers and knowledge graphs to senior business and technology stakeholders.

Experience contributing to sales, pre-sales, solution shaping, delivery leadership, stakeholder management, and enterprise data transformation programs.

Required Skills/ Experience

Deep knowledge of knowledge graph architecture, semantic modeling, ontology engineering, metadata management, data governance, graph curation, and graph-based AI/ML patterns.

Hands-on experience architecting GCP-based knowledge engineering solutions using BigQuery, Cloud Storage, Dataflow, Dataproc, Vertex AI, Cloud Run, Pub/Sub, IAM, APIs, and cloud security/monitoring services.

Strong Python expertise and hands-on experience with frameworks and tools such as PyTorch, TensorFlow, PySpark, Apache Airflow, Apache NiFi, SQL, SPARQL, SHACL, APIs, and ETL/ELT pipelines.

Ability to design scalable graph ingestion, schema/ontology pipelines, semantic data products, vector search/retrieval patterns, RAG grounding layers, and LLM-ready knowledge services.

Strong architecture leadership across cloud integration, data pipelines, security, governance, observability, cost optimization, reusable assets, and production readiness.

Good to Have Skills

Practical experience with NLP techniques, search techniques, prompt engineering, entity extraction, entity resolution, semantic search, and enterprise-scale LLM applications.

5+ years of hands-on experience with cloud platforms, with deep GCP specialization and working exposure to AWS or Azure in multi-cloud environments.

GCP certifications such as Professional Cloud Architect, Professional Data Engineer, Professional Machine Learning Engineer, or related credentials.

Industry experience in BFSI, healthcare, retail, telecom, manufacturing, energy, public sector, or life sciences, including industry-specific ontologies, data models, compliance needs, and knowledge-driven use cases.

Advanced degree or Ph.D. in Computer Science, Computer Engineering, Mathematics, Electrical Engineering, Data Science, or a related discipline.