Knowledge Graph Agentic AI Architect

Lorven Technologies Inc.; Cambridge (MA); Hybrid; USD 180,000 - 260,000; Full time

Job summary

Lorven Technologies Inc. in Cambridge, MA is seeking a Knowledge Graph Agentic AI Architect to lead enterprise data and AI strategy. The role is hybrid and requires deep expertise in graph databases, ontologies, and AI workflows.

You will design scalable platforms using Neo4j, Neptune, SQL databases, and cloud services while shaping governance, security, and observability. The position demands 12+ years of experience and strong leadership across stakeholders.

Qualifications

• 12+ years of experience in Data Architecture, AI/ML, Enterprise Data Platforms, or Data Engineering.
• Strong SQL database architecture, data modeling, query optimization, indexing, ETL/ELT design, and data warehousing.
• Hands-on experience with Knowledge Graphs, graph databases, ontology design, and semantic technologies.
• Experience with LLMs, vector databases, RAG architectures, and Agentic AI frameworks such as LangGraph, CrewAI, AutoGen, or Semantic Kernel.
• Proficiency in Python, SQL, APIs, cloud services (AWS, Azure, or GCP), and modern data engineering tools.
• Strong understanding of enterprise architecture, data governance, and AI solution design.

Responsibilities

• Design and implement enterprise Knowledge Graph solutions using graph technologies like Neo4j or Amazon Neptune.
• Architect scalable relational and analytical data platforms using SQL Server, PostgreSQL, MySQL, Snowflake, or cloud-native databases.
• Develop ontologies, semantic models, taxonomies, and entity relationship frameworks.
• Design Agentic AI solutions leveraging LLMs, multi-agent frameworks, RAG, Graph RAG, and autonomous workflows.
• Define enterprise data architecture, including data lakes, warehouses, vector databases, and metadata management.
• Build scalable data ingestion, transformation, enrichment, and knowledge extraction pipelines.
• Establish standards for data governance, security, performance optimization, observability, and AI reliability.
• Drive innovation through adoption of emerging AI, graph, and data technologies.

Skills

Data architecture

AI/ML

Graph databases

Ontology design

LLMs

Python

Cloud services

Data governance

Education

Bachelor’s or Master’s in CS/Data Eng/AI

Tools

Neo4j

Amazon Neptune

SQL Server

PostgreSQL

Snowflake

Job description

Job Title : Knowledge Graph Agentic AI Architect

Location : Cambridge, MA (Hybrid)

Duration : Long Term

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Data Engineering, AI, or a related field.
• 12+ years of experience in Data Architecture, AI/ML, Enterprise Data Platforms, or Data Engineering.
• Strong expertise in SQL database architecture, data modeling, query optimization, indexing, ETL/ELT design, and data warehousing.
• Hands-on experience with Knowledge Graphs, graph databases, ontology design, and semantic technologies.
• Experience with LLMs, vector databases, RAG architectures, and Agentic AI frameworks such as LangGraph, CrewAI, AutoGen, or Semantic Kernel.
• Proficiency in Python, SQL, APIs, cloud services (AWS, Azure, or GCP), and modern data engineering tools.
• Strong understanding of enterprise architecture, data governance, and AI solution design.

Preferred Skills

• Experience with Life Sciences, Healthcare, or other regulated industries.
• Familiarity with MLOps, LLMOps, and AI monitoring frameworks.
• Strong stakeholder management and technical leadership skills.

Key Responsibilities

• Design and implement enterprise Knowledge Graph solutions using Neo4j, Amazon Neptune, or similar graph technologies.
• Architect scalable relational and analytical data platforms using SQL Server, PostgreSQL, MySQL, Snowflake, or cloud-native databases.
• Develop ontologies, semantic models, taxonomies, and entity relationship frameworks.
• Design Agentic AI solutions leveraging LLMs, multi-agent frameworks, RAG, Graph RAG, and autonomous workflows.
• Define enterprise data architecture, including data lakes, warehouses, vector databases, and metadata management.
• Build scalable data ingestion, transformation, enrichment, and knowledge extraction pipelines.
• Establish standards for data governance, security, performance optimization, observability, and AI reliability.
• Drive innovation through adoption of emerging AI, graph, and data technologies.