Lead AI Engineer
Indexed description
Key Responsibilities
Knowledge Graph & Ontology (Neo4j / App Orchid)
- Design and implement ontology models and semantic frameworks
- Build and scale Customer Knowledge Graph using Neo4j and App Orchid
- Develop entity resolution, relationship mapping, and enrichment pipelines
- Write and optimize graph queries (Cypher) for analytics and insights
- Manage performance, scalability, and governance of KG platform
- Architect and implement Agentic AI and multi-agent systems
- Leverage LLMs and RAG with Knowledge Graph for contextual intelligence
- Enable capabilities such as:
- Customer 360 insights
- Relationship discovery & scoring
- Natural language querying (Graph/SQL agents)
- Drive end-to-end AI lifecycle (design → deploy → optimize)
- Build scalable pipelines to integrate enterprise data into KG
- Implement customer identity resolution and data quality frameworks
- Design APIs for application and AI model integration
- Lead AI platform architecture and roadmap
- Mentor engineering teams and enforce best practices
- Drive AI-first SDLC adoption and enterprise scaling
- Collaborate with business, data science, and engineering stakeholders
- Knowledge Graph & Ontology: RDF, OWL, semantic modeling
- Graph Platforms: Strong hands-on with Neo4j and App Orchid
- Graph Querying: Cypher (mandatory)
- AI/GenAI: LLMs, RAG, Agentic AI (CrewAI/LangGraph)
- Programming: Python (AI + data engineering)
- Data Engineering: Spark, Kafka, Airflow (or equivalent)
- Cloud: AWS / Azure
- MLOps/DevOps: CI/CD, scalable system design
- Customer 360 / Customer Data Platforms
- Graph analytics (community detection, centrality)
- Graph visualization tools
- Exposure to GNNs
- Docker / Kubernetes
- Own AI product/platform delivery end-to-end
- Define technical roadmap and architecture strategy
- Drive enterprise AI adoption with business impact (revenue, engagement)
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