Capabilities
Strong engineering foundation with practical AI implementation
RAG Systems
Retrieval pipelines with source citations, relevance testing, and practical latency budgets.
Agent Workflows
Tool-using agents with guardrails, retries, observability, and human escalation paths.
AI Evaluation
Evaluation loops for answer quality, hallucination control, and prompt regression tracking.
AWS Platform Engineering
Cost-aware cloud architecture across Lambda, API Gateway, DynamoDB, queues, and storage.
Local LLM Engineering
Practical local model workflows for secure experimentation and developer productivity.
API and Full-Stack Delivery
Production-minded web and API systems from architecture decisions to deployable code.
What I Build
From architecture decisions to working systems
- Retrieval-driven applications for internal knowledge and customer support
- Agent workflows for research, triage, and operational automation
- Evaluation pipelines that monitor quality and prevent silent model drift
- Cloud-hosted APIs for AI products with reliability and cost controls
- Developer tooling that speeds delivery without sacrificing maintainability
- Architecture prototypes that derisk roadmap decisions
Selected Work
Case studies that show technical depth, not just feature lists
In Progress
AWS RAG Tutor
Context-aware tutoring assistant backed by citation-first retrieval.
Next.jsAWS LambdaAPI GatewayDynamoDB
In Progress
Research Agent
Multi-step research workflow with tool orchestration and human checkpoints.
TypeScriptNode.jsAWS Step FunctionsS3
Planned
Local LLM Coding Assistant
Private coding assistant for secure repositories and offline-friendly workflows.
PythonLocal ModelsVS CodeVector Search
Shipped
LLM Evaluation Lab
Evaluation harness for quality, latency, and cost tradeoff decisions.
PythonPytestPandasAWS
Engineering Principles
How I evaluate technical decisions
Design for reliability first, then optimize complexity.
Use measurable quality gates for model outputs and retrieval relevance.
Choose AWS services based on team velocity and long-term operating cost.
Treat observability and security as first-class architecture concerns.
Career Focus
Building visible proof of architecture and implementation skill
This portfolio is designed to show real software engineering execution, architecture thinking, and applied AI learning. I am open to collaborating on the right consulting or contract projects, but the primary goal is showcasing high-quality technical work.