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