SOFTWARE ENGINEER / 2024 - NOW
Cobbs Creek Healthcare
AI-assisted healthcare tools and backend workflows.
Built AI-assisted healthcare tools, backend data workflows, LLM transcript extraction pipelines, and anomaly-detection systems using Python, Databricks, OpenAI, LangChain, and production-oriented APIs.
- Python
- Databricks
- OpenAI
- LangChain
- PySpark
CONTEXT
Healthcare workflows need useful AI, reliable data, and clear outputs.
The work focused on turning messy operational data and transcript-heavy workflows into systems that teams could inspect, trust, and act on.
SYSTEMS SHIPPED
01
AI-assisted transcript extraction
LLM workflows for turning transcript content into structured, reviewable outputs.
02
Weekly anomaly detection workflows
Scheduled checks for surfacing unusual healthcare data patterns.
03
Databricks and PySpark pipelines
Backend workflows for processing and validating data at practical scale.
04
Healthcare data quality tooling
Tools and checks that make operational data easier to review and explain.
TOOLS & SYSTEMS
LANGUAGES
- Python
- SQL
DATA
- Databricks
- PySpark
AI
- OpenAI
- LangChain
BACKEND
- APIs
- Docker
- Postgres
- Qdrant
WHAT I BUILT
Production-oriented AI and backend workflows for extracting, validating, and operationalizing healthcare data.
WHAT I LEARNED
- Design AI outputs for review and correction
- Build checks around messy operational data
- Keep backend workflows explainable for non-engineering users
- Balance experimentation with production constraints
WORK STATUS
ACTIVE ROLE
Current software engineering work across AI-assisted tools and backend workflows.
PRIVATE DETAILS
Public page uses placeholders and high-level descriptions only.