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Engineer working at the intersection of AI systems, high-performance backend and local LLM infrastructure. I build asynchronous Python pipelines that stay fast under load, self-hosted inference stacks that keep data private, and agentic workflows that actually ship to production — not just to a notebook. Currently focused on making local models genuinely practical for business: smart caching, containerized deployments, and orchestration layers that don't fall apart at scale. |
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High-throughput data pipelines using |
Self-hosted AI solutions with intelligent caching layers (PostgreSQL/JSONB + Redis) for cost-efficient, privacy-first inference at scale. |
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Multi-agent pipelines with CrewAI and LangChain — orchestrating autonomous agents for complex business automation tasks. |
Production-grade Docker/Docker Compose setups for reproducible AI environments, database services, and microservice architectures. |
- 🔥 Contribute to open-source AI infrastructure projects
- 🏗️ Deploy production-ready local LLM systems for real-world use cases
- 🤝 Collaborate on advanced agentic workflow tooling
- 📦 Publish reusable Python packages for async AI pipelines