About These Projects
At AWS, I designed and built AI-powered systems that automated complex workflows, achieving measurable productivity gains. These tools were developed for internal use and some were shared across the organization.
A Note on Samples
These tools were built for internal AWS workflows and are not available for external demonstration. What I can share is how they worked, the problems they solved, and the measurable impact they had. The experience I gained building these systems has only deepened since—I continue to explore and build with new AI capabilities as they emerge.
AI Training Courses
Beyond building tools, I created and taught AI training courses for AWS employees for major AI initiatives:
- Prompt engineering fundamentals: How to write effective prompts for documentation tasks and when and how to implement RAG
- Tool development: Building custom AI workflows for specific use cases
- Best practices: Quality control, fact-checking, and maintaining accuracy when using AI assistance
Training Impact
Post-course surveys showed 80%+ adoption rates of the techniques taught, with participants reporting 30%+ productivity gains in their workflows.
Projects
The following projects demonstrate different aspects of AI-powered tool development, from multi-stage pipelines to agentic systems:
Multi-Stage AI Drafting Pipeline
A multi-stage AI pipeline using LLMs and MCP that automated content drafting from initial draft through post-review revisions with built-in fact-checking.
View project →Agentic Ticket Resolution System
An agentic system that automated ticket analysis and resolution—parsing requests, navigating codebases, and generating targeted fixes.
View project →Promotion Documentation Assistant
A personal productivity tool that synthesizes accomplishments into structured STAR-format narratives for performance review documentation.
View project →Technical Skills Demonstrated
Building these tools required combining multiple skill sets:
- LLM Integration: Which foundation models for which purposes, prompt engineering, context management
- MCP Protocol: Using tools that extend LLM capabilities
- Python Development: Automation scripts, API integrations, file processing
- Workflow Design: Multi-stage pipelines with error handling and human-in-the-loop checkpoints
- Domain Expertise: Understanding end-user needs and content quality standards to design effective AI-assisted workflows
Continuing to Build
The AI landscape evolves rapidly, and so does my toolkit. I continue to experiment with new capabilities—from improved reasoning models to multi-agent architectures— always looking for ways to make knowledge workflows more efficient and effective.