AI AGENTS · SYSTEMS ENGINEER · ARCHITECT
AI Agents & Systems Architect
Outcome-oriented AI systems, built to run in production.
I design and build AI agents that operate within real workflows. Systems are structured around existing teams and processes, ensuring they remain practical to use while handling execution reliably under real-world constraints.
Who I am
AI agents are built as systems, not components.
I approach these systems by focusing on how they behave in real use—how decisions are made, how components interact, and how the system evolves over time. Each layer is designed with clear responsibilities, allowing the system to scale in a controlled and predictable way.
The result is agent-based systems that remain stable, maintainable, and reliable as they grow.
Voice Agents
Knowledge Agents
Workflow Automation
Agent Orchestration
Backend Architecture
Cloud Infrastructure
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<1s
Voice agent response latency
Pre-deployment QA
Issues detected before release
Multi-system
Agents integrated across tools
End-to-end
System design & architecture
Selected Work
Voice Infrastructure
Knowledge + Compliance
Satellite ML
Automation Systems
Four builds that show what production-grade AI actually looks like.
Each project combines AI logic with real architecture decisions, measurable outcomes, and systems thinking.
Phonx AI Voice Platform
Realtime voice automation for high-volume insurance workflows with compliance-aware orchestration and CRM sync.
<1s response
90%+ intent accuracy
60%+ call reduction
Read case study
Enterprise Vector Knowledge Graph
Graph-native knowledge retrieval for compliance-sensitive document intelligence — deployed in insurance, piloted in research.
Neo4j + vector embeddings
2 deployments
Audit-logged
Read case study
Satellite ML Poppy Detection
CNN-based poppy detection on Sentinel-2 imagery — a research POC built at Scanpoint Geomatics during my time there.
0.87 F1 score
Research POC
GIS shapefiles
Read case study
E-commerce Automation
AI-augmented email automation for a Shopify D2C brand — classification, draft generation, and confidence-based human escalation.
Shopify D2C
Confidence routing
Freelance · 2023
Read case study
Capabilities
Skills Overview
End-to-end design and execution of production AI systems — from intelligence layers to real-world deployment.
Intelligence Layer
This layer defines how systems think, reason, and make decisions. It includes orchestration of language models, structuring of knowledge, and designing how context is retrieved and applied in real-time environments.
LLM Orchestration
Prompt Engineering
Agent Design
RAG Systems
Context Retrieval
NLP Pipelines
Execution Layer
This is where AI systems interact with real users and workflows. It includes building APIs, integrating with external systems, and ensuring that intelligence is converted into real actions such as calls, responses, or transactions.
Production APIs
Microservices
REST & GraphQL
FastAPI / Node.js
Workflow Integrations
Infrastructure & Scaling
This layer ensures systems are stable, scalable, and production-ready. It focuses on handling concurrency, managing system load, and ensuring reliability under real-world conditions.
Docker
Kubernetes
Redis
PostgreSQL
Cloud (AWS EC2, RDS)
Product & System Design
This is where technical systems are aligned with business outcomes. It includes translating real-world problems into system architectures, defining requirements, and ensuring delivery from idea to execution.
System Architecture
Requirements Engineering
Stakeholder Alignment
Agile Execution
End-to-End Delivery
Toolbox
Built with the stack modern AI products actually run on.
A pragmatic toolkit across model APIs, backend services, infrastructure, and delivery.
Contact
Need an AI system that feels sharp to clients and solid underneath?
Let's talk about the build, the constraints, and the version of this system that is actually worth shipping.