Jennifer Cheng

GenAI/DevOps Expert | AWS Certified | LangChain & Vector Database Specialist

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Real Time Credit Card Fraud Detection Demo Chat bot for GraphRAG for fraud investigation

About Me

Jennifer Cheng

GenAI/DevOps Expert with Fraud Detection Expertise

I'm a highly skilled GenAI/DevOps Expert with extensive experience in building and managing AI-driven applications, specializing in AWS, LangChain, AIOps, and Vector Databases. Currently working at Neo007, I lead the development of real-time credit card fraud detection systems using graph-based analysis and generative AI.

My expertise spans across multiple domains including:

  • Building generative AI applications using LLMs and LangChain
  • Implementing and optimizing vector databases for efficient similarity search
  • Designing and managing cloud-native infrastructure on AWS
  • Developing conversational AI interfaces with RASA and AWS Lex
  • Creating end-to-end MLOps pipelines for continuous model improvement

With a background in both AI engineering and DevOps, I excel at bridging the gap between cutting-edge AI research and production-ready systems that deliver real business value.

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Neo4j Achievements

Revolutionizing Fraud Detection with Graph Databases

At Neo4j, I designed and implemented a groundbreaking graph-based system for identifying complex fraud patterns in financial transactions. By leveraging the power of graph algorithms and integrating conversational AI interfaces, I created a solution that dramatically improved fraud detection capabilities.

65%
Faster Detection
42%
Improved Accuracy
$4.2M
Annual Savings

My approach combined graph database technology with machine learning algorithms to identify suspicious patterns in transaction networks. By implementing a RASA-powered conversational interface, I enabled fraud analysts to investigate potential fraud cases through natural language queries, significantly reducing investigation time.

View 60-Second Pitch Presentation

Featured Projects

Intelligent Customer Support System
Intelligent Customer Support System

A comprehensive customer support solution leveraging RAG (Retrieval-Augmented Generation) and vector search to provide accurate, context-aware responses to customer inquiries.

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AWS Bedrock, Pinecone
AIOps Platform
AIOps Platform for ML Model Monitoring

An automated platform for monitoring ML model performance, detecting drift, and implementing automated remediation strategies to ensure consistent model quality.

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MLflow, AWS SageMaker
Multi-Modal GenAI Application
Multi-Modal GenAI Application

A scalable multi-modal generative AI application capable of processing and generating content across text, images, and structured data with a distributed vector database.

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Weaviate, Kubernetes
Integrated Fraud Detection System
Integrated Fraud Detection System

A comprehensive fraud detection platform combining real-time transaction monitoring, graph-based pattern recognition, and conversational AI interfaces for fraud analysts, with continuous improvement through MLOps.

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Neo4j, AWS Bedrock, Terraform
Conversational AI for Fraud Analytics
Conversational AI for Fraud Analytics

A specialized conversational interface enabling fraud analysts to investigate suspicious transactions through natural language queries, with RAG-powered knowledge retrieval and real-time transaction analysis.

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RASA, AWS Lex, LangChain

Technical Skills

GenAI & LLMs

LangChain AWS Bedrock RAG Prompt Engineering Fine-tuning Embeddings Multi-modal AI LLM Evaluation

DevOps & Cloud

AWS Terraform Docker Kubernetes CI/CD SageMaker Lambda ECS/EKS

Databases & MLOps

Neo4j Pinecone Weaviate FAISS MLflow DVC Model Monitoring Feature Stores

Conversational AI

RASA IBM Watson Assistant AWS Lex Intent Classification Entity Extraction Dialog Management Contextual Understanding Conversational Design

Programming & Tools

Python SQL Bash JavaScript Git Jenkins Ansible Jupyter

Interview Preparation Resources

Interactive Q&A Practice

Practice answering common interview questions with interactive timers and example responses.

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Interview Feedback Hub

Access comprehensive analysis and improvement strategies from five different interviews.

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60-Second STAR Pitch

Practice delivering a concise, impactful pitch about your Neo4j achievements using the STAR method.

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Get In Touch

Let's Connect

Interested in working together or have questions about my projects?

Email

jennifercheng2001@gmail.com

GitHub

github.com/Jencheng1

LinkedIn

linkedin.com/in/jennifer-cheng