AVAILABLE FOR NEW WORK

Designing deterministic workflows
for AI agents.

Applied AI Systems Architect.

01 / About

Building software
that solves real problems.

I operate at the intersection of machine cognition and human agency. Most modern AI products expose the raw, chaotic mechanics of underlying models. I believe software should tame that chaos—delivering high-utility, predictable, and deeply respectful interactions.

I write robust, multi-agent state machines, optimized retrieval schemas, and evaluation harnesses. My work is built to be fast, production-ready, and architected to safeguard user attention instead of taxing it.

// 01

Human first, model second

AI should elevate and extend human capability, not replace or simulate it. We construct software to empower human intent, not to create automated noise.

PRINCIPLE_METRIC
// 02

Deterministic guardrails

Stochastic models produce unpredictable results. We wrap intelligence in mathematical guardrails, ensuring reliability in high-stakes environments.

PRINCIPLE_METRIC
// 03

Performance is respect

Lag is cognitive drag. Orchestration, retrieval, and interface rendering are optimized for zero latency, respecting the flow state of the operator.

PRINCIPLE_METRIC
02 / Featured Projects
03 / Engineering Case Study

Designing scalable software for production environments.

Role

Lead AI Engineer

Timeline

12 Weeks (Q1 2026)

Technologies

Python, LangGraph, Qdrant, Claude 3.5 Sonnet, AWS ECS

The Context

Enterprise workflows were heavily dependent on manual data entry and unstructured review steps, creating a 12-hour turnaround bottleneck. Automated scripts failed to handle variance in document formats and unstructured inputs.

The Challenge

Parsing unstructured data tables and files into strict schema formats. Standard retrieval-augmented generation suffered from context leakage and output hallucination loops, causing errors in downstream production databases.

01 / The Problem

Manual Audit Review

Unstructured tables, emails, and invoices create human bottlenecks with high turnaround times and variance errors.

12h latency
02 / Orchestrated AI Pipeline
📥Input
🔍Understanding
🧠Reasoning
🛡️Validation
🚀Deployment
SYSTEM_METRICS

Execution Profile

AWAITING TRIGGER...
STATUS
IDLE
Legacy Pipeline
Manual Document Audit
Time: 12 HoursUnstructured
AI workflow
Autonomous State Machine
Time: ---Schema Verified
04 / Technical Skills
05.1 / Professional Experience
05.2 / Education
08 / Engineering Process

How I design and build production systems.

A live simulation of the complete request lifecycle—from query ingestion to production processing and delivery.

›_Query
Receives and validates the user's natural language input.
Understanding
Classifies intent and extracts semantic meaning from the prompt.
Knowledge
Retrieves relevant context from memory and vector databases.
Thinking
Decomposes the problem through chain-of-thought reasoning.
Tools
Executes function calls and external API integrations.
Response
Streams the verified output token by token to the user.
How can I build a production AI assistant?
Awaiting next request...
09 / Certifications
0%
AWS Certified Machine Learning – Specialty. Validated competency in designing, building, and deploying deep learning pipelines on AWS infrastructure.
0%
Generative AI Developer (DeepLearning.AI). Advanced skills in constructing RAG models, LangGraph agentic frameworks, and fine-tuning.
0+
Google TensorFlow Developer. Certified proficiency in engineering deep neural networks, computer vision models, and sequence processing.
10 / Technical Writing