# Chris Austin - Senior AI Engineer

## Overview
Chris Austin is a Senior AI Engineer with 7+ years of experience building scalable data systems and AI solutions, from ML pipelines to multi-agent GenAI apps. Experienced in Python, Spark, SQL, and deploying across Azure, AWS, and hybrid environments.

## Current Role
Senior AI Engineer at Cognizant.

## Location
Los Angeles / Remote

## Education
B.S. in Mathematics and Computer Science from Temple University.

## Contact
- Email: ceaustin117@gmail.com
- LinkedIn: linkedin.com/in/christopher-austin
- GitHub: github.com/ceaustin117

## Featured Projects

### Multi-Agent GenAI Tax Automation
Designed a multi-agent GenAI architecture on Azure that automated tax document workflows, cutting review time from 14 days to 1 day, and enabling real-time AI approval through a React web app.
Technologies: Azure, GenAI, Multi-Agent Systems, React, Python

### LLM vs Rule-Based Classification
Built a classification pipeline that compares LLM vs rule-based outputs to cut manual review time by 40% and reached over 95% accuracy.
Technologies: LLM, Python, ML Pipelines, Azure

### Microsoft Fabric Analytics Solution
Delivered a Microsoft Fabric analytics solution with semantic search and AI insights, improving decision-making speed and accuracy across operational sources.
Technologies: Microsoft Fabric, Azure, Semantic Search, AI

### Databricks MLOps Pipeline
Rebuilt legacy ML pipelines within a modern Databricks MLOps lifecycle framework (MLflow, CI/CD), adding LLM-based monitoring and improving deployment speed.
Technologies: Databricks, MLflow, CI/CD, Python

### Cloud ML Demand Forecasting
Built a cloud ML pipeline for demand forecasting, cutting modeling time from hours to minutes and delivering forecasts via a React dashboard.
Technologies: AWS, ML Pipelines, React, CI/CD

### Predictive Vehicle Mapping Pipeline
Engineered a predictive mapping pipeline using Azure Data Factory and Python, integrating ML models that improved processing speed by 500% across millions of vehicles.
Technologies: Azure Data Factory, Python, ML, Spark

## Key Achievements
- Reduced tax document review time from 14 days to 1 day
- Achieved 500% processing speed improvement on vehicle mapping pipeline
- Reached 95%+ classification accuracy on LLM pipeline
- 7+ years of professional experience in data and AI

## Skills
- GenAI & LLMs: LangChain, Multi-Agent Systems, RAG, Prompt Engineering
- ML Engineering: ML Pipelines, MLflow, Forecasting, Classification
- Cloud: Azure (Data Factory, ML, Functions, Fabric), AWS, Databricks, Snowflake
- Programming: Python, SQL, Spark/PySpark, TypeScript, JavaScript

## Certifications
- Microsoft Azure AI Engineer
- Microsoft Azure Fabric Data Engineer
- AWS Certified Cloud Practitioner
- Databricks Generative AI Engineer

## This Website's RAG Chatbot Architecture

This chatbot you're talking to is a RAG (Retrieval-Augmented Generation) system built by Chris Austin to demonstrate AI engineering skills.

### Architecture Overview
The system consists of two main components:
1. **Frontend**: React TypeScript app hosted on GitHub Pages
2. **Backend**: Python FastAPI server hosted on HuggingFace Spaces

### How It Works
1. User types a question in the chat interface
2. Frontend sends the question to the backend API
3. Backend embeds the question using sentence-transformers (all-MiniLM-L6-v2)
4. Cosine similarity search finds the most relevant chunks from the knowledge base
5. Top 3 relevant chunks are sent as context to the LLM
6. Groq API (llama-3.1-8b-instant) generates a response based on the context
7. Response is returned to the frontend and displayed

### Tech Stack
- **Frontend**: React 19, TypeScript, CSS, GitHub Pages
- **Backend**: Python, FastAPI, uvicorn
- **Embeddings**: sentence-transformers (all-MiniLM-L6-v2 model)
- **Vector Store**: In-memory with pre-computed embeddings stored in JSON
- **LLM**: Groq API with Llama 3.1 8B Instant model
- **Hosting**: HuggingFace Spaces (backend), GitHub Pages (frontend)

### Why This Architecture
- **Cost**: $0 - uses free tiers of Groq and HuggingFace
- **Simplicity**: No complex vector database needed for small knowledge base
- **Speed**: Groq provides fast inference, sentence-transformers are lightweight
- **Customizable**: All code is custom Python, easy to modify and extend

### Key Design Decisions
- Pre-computed embeddings: Knowledge base is small (~6 chunks), so embeddings are computed once and stored in JSON
- In-memory search: Simple numpy cosine similarity, no need for Pinecone/Chroma
- Groq over OpenAI: Faster and has generous free tier
- Separate frontend/backend: Clean separation, backend can be reused for other interfaces