Available for Contract & Full-time Roles

Hi, I'm Your Name.
I build Knowledge Graphs & GraphRAG AI.

Neo4j Certified Graph Data Science Engineer. Specializing in connecting complex enterprise unstructured data, graph algorithms, and LLM reasoning pipelines to eliminate hallucinations.

⚡ Neo4j Certified Pro & GDS ⚡ Multi-Modal GraphRAG ⚡ Cypher Optimization

View Projects Book 15-Min Call Download CV

About Me

Engineering Connected Intelligence

I bridge the gap between complex network theory and enterprise AI. Relational databases break down when data becomes heavily interconnected, and standard vector search fails at multi-hop reasoning.

I specialize in Neo4j Graph Data Science (GDS), knowledge graph extraction pipelines, and hybrid GraphRAG architectures that give LLMs structured, explainable ground truth.

Toolkit

Graph & AI Tech Stack

Neo4j & Cypher

Aura, GDS, APOC

Graph ML

NetworkX, FastRP, Louvain

LLMs & GraphRAG

Gemini, OpenAI, LangChain

Python Backend

FastAPI, Pandas, Streamlit

Data Pipelines

Multi-format ETL, Embeddings

Cloud & Deploy

Vercel, Docker, Git

Case Studies

Featured Graph Engineering Projects

Neo4j Aura GDS Google Gemini Flash GraphRAG

Universal GraphAI Platform

Enterprise Knowledge Graph & Intelligence system. Ingests PDF, DOCX, CSV, TXT, MD files, builds connected entity graphs in Neo4j Aura Cloud, and performs hybrid GraphRAG retrieval with Google Gemini to resolve complex multi-hop queries.

5+ Formats

PDF, DOCX, CSV, TXT, MD

< 300ms

Graph Context Lookup

0% Stored Temp

Auto Disk Privacy Cleanup

GitHub Code Live Demo

Cypher Knowledge Schema AuraDS Cloud

// Graph Context Augmented Prompt

MATCH (doc:Document)-[:MENTIONS]->(e:Entity)
WHERE e.name = "SupplyChain_Risk"
RETURN e, relationships(e)

Ground truth context delivered to Gemini

GDS Algorithms Fraud Detection

Financial Circular Transaction & Fraud Ring Detector

Identified money laundering and synthetic identity rings in 100k+ bank transactions using Neo4j Louvain Community Detection and Weakly Connected Components (WCC).

99.2% Ring Detection View Case Study

FastRP / Node2Vec Recommendation

Graph-Powered Hybrid Recommendation Engine

Combined collaborative filtering with topological node embeddings (FastRP) in Neo4j to generate context-aware product and content recommendations with explainability.

35% Relevance Lift View Case Study

Verified Credentials

Official Neo4j Certifications

CLICK TO VERIFY ↗

Neo4j Certified Professional

Cypher Query Tuning, Graph Data Modeling, Subgraphs & APOC.

Issued by Neo4j • GraphAcademy