Neo4j Certified Graph Data Science Engineer. Specializing in connecting complex enterprise unstructured data, graph algorithms, and LLM reasoning pipelines to eliminate hallucinations.
About Me
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 provide LLMs with structured, explainable ground truth.
Toolkit
Aura, GDS, APOC
NetworkX, PyVis, Louvain
Gemini, OpenAI, Vision OCR
FastAPI, Pandas, Streamlit
Multi-format ETL, Chunking
Vercel, Streamlit, Git
Flagship Systems
Live interactive platforms powered by Neo4j & Google Gemini
An end-to-end intelligence system that ingests unstructured enterprise dossiers (PDF, DOCX, CSV, TXT, MD), builds dynamic property graphs in Neo4j Aura Cloud, and performs hybrid 2-hop GraphRAG reasoning with Google Gemini to guarantee zero hallucinations and deterministic audit trails.
An enterprise supply chain and maritime routing engine modeling 24 global seaports, inland intermodal rail hubs, and critical maritime chokepoints. Dynamically solves multi-objective path traversals balancing lead time, freight cost ($/TEU), and Scope 3 ESG emissions (kg CO₂) with a 4-scenario geopolitical disruption simulator (Suez/Red Sea, Strait of Hormuz, Panama Canal, and Strait of Malacca).
An enterprise anti-money laundering (AML) and financial crime intelligence platform that detects circular fund laundering (round-tripping), synthetic identity device farms, and structured smurfing funnels. Integrates Neo4j GDS pattern traversals with Google Gemini to autonomously generate auditable, FinCEN-standard Suspicious Activity Reports (SAR).
An event-driven transaction surveillance engine that intercepts circular fund laundering loops and synthetic device farms in-flight (< 20ms) before settlement. Uses an axiomatic graph reasoning layer in Neo4j to dynamically quarantine mule accounts, paired with a grounded Gemini agent that auto-generates regulatory Suspicious Activity Reports (SAR) backed by an automated 0.0% hallucination eval testbench.
What I Offer
Available for contract engineering & full-time technical roles
Transforming siloed relational SQL databases and unstructured documents into high-performance Neo4j property graph schemas and robust ETL pipelines.
Connecting LLMs (Gemini, OpenAI, Claude) with Knowledge Graph topologies to execute complex multi-hop reasoning with zero hallucinations and verifiable citations.
Applying Neo4j GDS algorithms (Louvain, PageRank, FastRP node embeddings) for community detection, fraud ring identification, and recommendation engines.
Profiling Cypher execution plans, building index-backed traversals, and optimizing sub-graph queries for high-throughput enterprise performance.
Thought Leadership
Exploring how combining Knowledge Graphs with LLMs eliminates hallucinations, unlocks multi-hop inference, and provides verifiable audit trails.
How Graph Data Science models maritime corridors, simulates geopolitical crises (Suez, Hormuz, Panama), and optimizes multi-objective freight trade-offs.
Uncovering circular layering cycles, synthetic identity device farms, and smurfing mule networks to generate FinCEN-standard Suspicious Activity Reports (SAR).
Why overnight batch monitoring fails, and how sub-20ms graph reasoning combined with grounded Gemini agents intercepts circular laundering rings before settlement.
Verified Credentials
Cypher query tuning, Graph Data Modeling, Subgraphs & APOC.
Issued by Neo4j
Graph algorithms, Louvain, FastRP node embeddings, ML pipelines.
Issued by Neo4j
Automated workflows, system scripting, Git, and data pipelines.
Issued by Google
Structured prompt design, zero-shot/few-shot techniques, and LLM grounding.
Issued by Google
Get In Touch
Looking to hire a Neo4j Graph Data Science Engineer or need consulting on GraphRAG and Knowledge Graph architectures? Let's connect.