I build agentic AI systems that combine LLMs, retrieval, memory, multimodal document processing, automation, and infrastructure.
My focus is practical AI: systems that read and search private data, use tools, keep state, and run reliably across self-hosted and cloud environments.
- Agentic RAG and document intelligence
- Multimodal OCR and metadata extraction
- Long-term memory systems for AI agents
- Self-hosted AI infrastructure and model serving
- Workflow automation with LLM-powered tools
I work on AI systems that connect language models to real infrastructure, private data, and long-running workflows.
- Agentic systems: tool use, memory, retrieval, orchestration, and stateful workflows.
- Document intelligence: OCR, metadata extraction, vector search, and RAG over private archives.
- Self-hosted AI infrastructure: local inference, model serving, Docker-based deployment, and observability.
- Automation systems: bots, background workers, scheduled jobs, and operational tooling.
- Infrastructure as code: Proxmox, VyOS networking, Puppet, Vault, FreeIPA, backups, and monitoring.
- Languages: Python, Rust, Shell
- AI/LLM: RAG, embeddings, vector search, OCR, multimodal pipelines, agent memory, training, finetuning
- Infrastructure: Docker, Proxmox, Puppet, Ansible, VyOS, Vault, FreeIPA
- Operations: Linux, networking, observability, backups, self-hosted services
- Cloud: Google, Azure



