GE Vernova
From GE Vernova's careers page
Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
Responsible for programing a component, feature and or feature set. Works independently and contributes to the immediate team and to other teams across business. You will also contribute to design discussions.
Roles and Responsibilities
Build the AI Context Layer: Develop and maintain backend services and data pipelines that supply relevant business context, metadata, and constraints to LLM applications.
Work with Semantic Models & Ontologies: Help implement, update, and query domain ontologies and taxonomies using standards like RDF, OWL, or SPARQL under the guidance of senior architects.
Knowledge Graph Integration: Write code to interact with graph databases (e.g., Neo4j, AWS Neptune) to support semantic search and relationship mapping.
Enhance Search & RAG Pipelines: Help build and optimize hybrid retrieval systems combining vector search (semantic similarity) with structured graph data for better LLM grounding.
Data Pipelines: Write robust data ingestion and processing scripts to extract entities and relationships from various enterprise data sources.
Collaboration: Partner with ML engineers, data scientists, and backend developers to integrate semantic components into production AI products.
Code Quality: Write clean, testable, and well-documented code following best software engineering practices.
Required Qualifications & Skills
Experience: 4–6 years of software development experience, with at least 1–2 years working on data-heavy applications, NLP, vector search, or semantic/graph technologies.
Programming: Strong backend development skills in Python (preferred for AI workflows) and/or Java/TypeScript.
Databases & Storage: Experience working with relational databases and at least some exposure to NoSQL, vector databases (e.g., Pinecone, Milvus, Qdrant), or graph databases (e.g., Neo4j).
Foundational Knowledge of Semantics/AI: Familiarity with concepts like knowledge graphs, vector embeddings, prompt engineering, or semantic web standards (RDF/OWL). We don't expect an ontology expert, but you should be eager to learn and work with them.
API Development: Experience building and consuming RESTful APIs and microservices.
Education: Bachelor’s degree in computer science, software engineering, or a related technical field (or equivalent practical experience).
Relocation Assistance Provided: Yes
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