Knowledge Graphs:
Experience with building and querying knowledge graphs.
Proficiency in languages and tools such as (Geo)SPARQL, RDF and OWL
Understanding of ontologies and semantic web technologies.

Machine Learning:
Solid foundation in machine learning algorithms and methodologies.
Experience with popular ML frameworks such as TensorFlow and PyTorch
Ability to apply ML techniques to graph data.
In-depth knowledge of Transformer model

Graph Neural Networks (GNN):
Hands-on experience with GNNs, including familiarity with libraries like PyTorch Geometric or DGL.
Understanding of different GNN architectures and their applications.
Experience in implementing and optimizing GNN models for various tasks.

Data Handling and Preprocessing:
Skills in data cleaning, transformation, and preparation, especially for graph data.

Programming Languages:
Proficiency in Python, with experience in libraries and tools for data manipulation and analysis (e.g., Pandas, NumPy)

📌 Knowledge Graph Engineer Bengaluru
🏢 BMW TechWorks
📍 Bengaluru