Applied AI Engineer focused on LLM products, RAG systems, agentic workflows, backend AI services, and robotics experimentation. I build practical systems that turn messy real-world problems into reliable tools people can actually use.
- 👨💻 Projects & portfolio: https://danielz.co.uk
- 📝 Technical writing & blog: https://danielz.co.uk/blog/
- 📄 CV: https://danielz.co.uk/cv/
- 📫 Reach me at: danial.za@outlook.com
- ⚡ Fun fact: I like building AI systems that do real work, not just nice demos.
- LLM-powered products and copilots
- RAG pipelines, semantic retrieval, and vector search
- Agentic workflows, tool use, memory, and orchestration
- Python backend engineering with FastAPI and APIs
- AI evaluation, structured outputs, and reliability
- Reinforcement learning, sim-to-real workflows, and robotics experimentation
- Applied AI Engineering — building production-oriented AI systems for real business and user workflows
- Agentic AI — multi-step workflows, tool-connected systems, memory-aware interaction, and orchestration
- Retrieval Systems — RAG, semantic search, vector databases, grounded responses, and explainability
- Backend & Platform Work — FastAPI services, integrations, automation, deployment-aware system design
- Robotics & RL — MuJoCo, ROS, Niryo NED3 Pro, sim-to-real evaluation, and control-focused experimentation
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- Building reliable AI assistants and agentic workflows
- Improving retrieval, memory, and evaluation in LLM systems
- Exploring AI product engineering across backend, orchestration, and UX
- Working on reinforcement learning and robotics projects with sim-to-real thinking