
Software Engineer, Data - Query Intelligence & Infrastructure - 6 Month Contract
- London Cambridge
- Permanent
- Full-time
- Design, implement, and own the Model Context Protocol, ensuring our GenAI systems receive rich, stateful information for advanced query interpretation.
- Build and leverage knowledge graphs to model complex data relationships, enhancing the depth and accuracy of our query understanding capabilities.
- Develop and maintain robust data pipelines that populate our graph databases and support efficient, real-time query analysis.
- Develop and refine APIs that provide fast, reliable access to processed data and insights derived from our graphs and ML models.
- Establish, monitor, and improve query performance benchmarks against large-scale graph and tabular datasets.
- Contribute to query interpretation and NLP-driven analysis, applying ML techniques to improve system intelligence.
- Design and implement scalable, testable solutions using modern frameworks and tools.
- Collaborate with colleagues on the design and delivery of data-intensive ML systems that are fundamentally graph and context aware.
- Share knowledge and learn from others to continuously raise the technical bar.
- PhD or advanced degree in Computer Science, Software Engineering, or a related field.
- A strong background in machine learning, NLP, query analysis, and data science applications.
- Experience with graph technologies, including graph databases (e.g., Neo4j, Neptune) and/or graph processing frameworks (e.g., NetworkX).
- Experience designing or working with systems that manage state or context for GenAI models; direct experience with a Model Context Protocol is a significant plus.
- Hands-on experience in Python
- Proficiency in query languages (e.g., SQL, Cypher, Gremlin) and optimizing queries over large, complex datasets.
- Experience building or supporting ETL pipelines, APIs, or real-time/batch query systems.
- Familiarity with cloud environments (GCP preferred), CI/CD pipelines, Docker, and GitHub Actions.
- A strong understanding of performance optimization.
- The ability to clearly communicate technical concepts to both technical and non-technical audiences.
- A collaborative mindset and a growth mindset, staying up to date with advances in ML, graph systems, and data engineering.