GCP AI Platform Engineer

Description: Knowledge Graph & AI Engineer Overview We are seeking an experienced Knowledge Graph & AI Engineer with deep expertise in graph technologies, ontology design, and modern AI integration. This role focuses on building scalable knowledge graph architectures, developing graph queries, integrating structured and semi-structured data, and enabling AI-driven insights through RAG and LLM-powered applications. The ideal candidate has experience in data engineering, AI engineering, graph data modeling, and deploying machine learning solutions in cloud environments., * Design and implement knowledge graph architectures using property graph or RDF-based models.

• Transform and integrate structured and semi-structured data into optimized graph structures.
• Develop and query graph systems using Cypher and/or SPARQL.
• Design ontologies and entity-relationship models to support sales intelligence and related use cases.
• Integrate knowledge graphs with LLMs using Retrieval-Augmented Generation (RAG) patterns.
• Build APIs and backend services to deliver AI-driven prospecting and recommendation insights.
• Implement scoring models, relationship strength analytics, and graph traversal logic.
• Ensure scalability, security, and performance across enterprise systems.
• Partner with sales, business, and engineering teams to translate requirements into technical solutions.

Do you have experience in Relational databases?, * 5+ years of experience in data engineering, AI engineering, or knowledge graph development.

• Hands-on experience with graph technologies, including property graph and/or RDF frameworks.
• Proficiency with Cypher and/or SPARQL.
• Strong data modeling and ontology design skills.
• Experience integrating data from relational databases and external sources.
• Strong Python development experience.
• Experience integrating LLMs into enterprise applications.
• Familiarity with Retrieval-Augmented Generation (RAG) architectures and AI-driven recommendation systems.
• Experience with Amazon SageMaker, AWS Machine Learning tools, ML pipelines, MLOps, CI/CD, model deployment, and inference endpoints.
• Experience with Docker, Kubernetes, and EKS.
• Industry experience in insurance, claims, underwriting, or fraud detection., * Experience building sales intelligence or CRM-adjacent platforms.
• Knowledge of embeddings, semantic search, and vector databases.
• Experience designing relationship scoring or network analytics models.
• Cloud experience with AWS, Azure, or Google Cloud Platform.
• Experience working in financial services or insurance environments.

Success Criteria

• Deliver a scalable knowledge graph and AI solution that improves sales prospect identification and relationship insights.
• Demonstrate measurable increases in targeting precision and cross-sell opportunity discovery.
• Establish reusable architectural patterns to support enterprise AI-driven sales initiatives.