Job Title: Databricks Knowledge Graph Engineer

Experience: 5+ Years

Employment Type: Full-Time

Job Summary We are looking for a Databricks Knowledge Graph & AI Engineer to design and develop knowledge engineering, semantic, graph, and AI solutions on the Databricks Lakehouse platform. The role involves building knowledge graphs, ontologies, semantic data products, vector search, LLM grounding, and RAG solutions that integrate with enterprise data architectures.

The ideal candidate should have strong hands-on experience with Databricks, Python, SQL, PySpark, Knowledge Graphs, Ontologies, Semantic Modeling, Vector Search, LLMs, and RAG .

Key Responsibilities

- Build knowledge engineering components using Databricks, Delta Lake, Unity Catalog, Databricks SQL, Workflows, MLflow, notebooks, jobs, Vector Search, and Model Serving.
- Design and develop Knowledge Graph, ontology, schema, taxonomy, metadata, and semantic-layer solutions.
- Develop lakehouse-based graph ingestion and semantic data pipelines.
- Build graph data models, mappings, validation rules, and knowledge graph components.
- Implement vector search, semantic search, RAG, and LLM grounding solutions.
- Develop scalable AI and knowledge engineering pipelines using Python, SQL, Spark/PySpark, APIs, and cloud storage.
- Integrate relational databases, graph databases, vector databases, and enterprise data sources.
- Design and implement end-to-end data pipelines supporting LLM-enabled and enterprise knowledge applications.
- Collaborate with architects, engineers, delivery teams, business stakeholders, and UI teams to deliver end-to-end solutions.
- Troubleshoot technical issues and ensure data quality, validation, governance, and scalability.
- Document technical designs, implementation approaches, validation results,



and data-quality observations.
- Contribute to technical demos, accelerators, reusable assets, and solution prototypes.
- Stay current with Generative AI, Agentic AI, LLMs, multimodal models, Graph RAG, semantic search, and Knowledge Graph technologies.
- Share technical knowledge and development best practices with team members.

Must-Have Skills
- Databricks
- Knowledge Graph
- Ontology & Semantic Modeling
- Python
- SQL
- Spark / PySpark
- RDF / SPARQL
- SHACL / OWL
- Vector Search / Vector Databases
- LLMs / Generative AI
- RAG / Semantic Search
- Data pipelines and ETL

Positive-to-Have Skills
- Neo4j, Stardog, Amazon Neptune or equivalent graph databases
- MLflow
- Databricks Unity Catalog
- Databricks Model Serving
- TensorFlow / PyTorch
- Apache Airflow / Apache NiFi
- Prompt Engineering
- Graph RAG
- Agentic AI
- REST APIs
- Cloud storage integrations

Required Experience
- 5+ years of experience with Knowledge Graph technologies such as RDF, SPARQL, LPG, SHACL, OWL, graph query languages, ontology management, and KG curation.
- 4+ years of experience in schema design, ontology management, semantic modeling, taxonomy, metadata management, and Knowledge Graph development.
- 4+ years of experience developing Knowledge Graph and graph-based ML solutions.
- 4+ years of experience with Python, SQL, and Spark/PySpark.
- 4+ year of experience developing end-to-end data pipelines for AI/LLM-enabled applications.
- Experience with relational, graph, and vector databases.
- Hands-on experience with Databricks-based knowledge engineering solutions.
- Practical experience with LLMs, RAG, vector search, semantic search, and NLP.

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

📌 Databricks Knowledge Graph Engineer (Bengaluru)
🏢 PineQ Lab Technology
📍 Bengaluru