AI/ML Engineer

Must Have Technical/Functional Skills • Technical Skills 5+ years in data engineering, AI engineering, or knowledge graph development

• Hands-on experience with knowledge graph technologies (property graph and/or RDF frameworks)
• Experience with Cypher and/or SPARQL
• Strong data modeling and ontology design skills
• Experience integrating relational databases and external data sources
• Strong Python development skills
• Experience integrating LLMs into enterprise applications
• Familiarity with RAG architecture and AI-driven recommendation systems

Roles & Responsibilities

• Design and implement knowledge graph architecture using property graph and/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 use cases
• Integrate knowledge graphs with LLMs using Retrieval-Augmented Generation (RAG) architecture
• Build APIs and application services to deliver AI-driven prospecting insights
• Implement scoring, relationship strength analysis, and network traversal logic
• Ensure scalability, security, and enterprise-grade performance
• Partner with sales and business stakeholders to translate requirements into technical solutions

Preferred Qualifications

• 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 platform experience (AWS, Azure, or GCP)
• Experience in financial services or insurance environments.

Success Criteri

• Deliver a scalable knowledge graph + AI solution that enhances sales prospect identification and relationship insights
• Enable measurable improvements in targeting precision and cross-sell opportunity identification
• Establish a reusable architecture pattern for enterprise AI-driven sales initiatives ClifyX