I started building software 7 years ago and have spent the last few years specializing in AI infrastructure, LLM systems, and distributed backend architectures.
My focus is on building production-grade AI systems — from document extraction pipelines processing thousands of files daily to multi-agent orchestration frameworks that power enterprise intelligence platforms.
AI & Machine Learning
I design and build end-to-end AI systems that go beyond prototypes — systems that handle real traffic, real data, and real failure modes at scale.
Core competencies include:
- LLM Systems — prompt engineering, fine-tuning, guardrails, cost optimization
- RAG Pipelines — hybrid search, chunking strategies, retrieval relevance tuning
- Agent Architectures — multi-agent orchestration, tool design, workflow patterns
- Document Intelligence — extraction, classification, post-processing pipelines
- Evaluation & Monitoring — LLM-as-Judge, drift detection, assertion-based testing
Infrastructure & Backend
I architect distributed systems that are resilient, observable, and cost-efficient:
- Workflow Orchestration — Temporal for complex multi-step AI pipelines
- Async Architectures — event-driven processing, message queues, async services
- Cloud-Native — AWS (SageMaker, Lambda, ECS), containerized deployments
- API Design — FastAPI, high-throughput async services, WebSocket systems
- Observability — monitoring, alerting, pipeline reliability systems
Detail and Summary
I represent all data in labels to make it easier to read. The underline indicator shows how often I used the related item, e.g.: