Profile
Building AI systems that retrieve, reason, and act.
I like figuring out how modern AI systems actually work when you put them together, not just in theory but in real applications. I spend most of my time working across machine learning and AI stacks—especially LLMs, retrieval-augmented generation (RAG), vector databases, and agent-based architectures—where the goal is not just to generate outputs, but to create systems that can retrieve the right context, reason over it, and take meaningful actions.
I approach projects from an engineering perspective: designing end-to-end pipelines that connect data, retrieval, and model behaviour into a coherent system. This includes building LLM pipelines, grounding responses through vector search, and developing agent workflows that can plan, break down tasks, and execute them reliably.
Currently, the direction is toward agentic AI—systems that combine reasoning, memory, and tools to operate with a level of autonomy. Most of this understanding comes from hands-on work, building and refining projects that reflect how these systems are used in real-world scenarios.
Tech Stack
Core technologies behind the systems I build.
A focused stack across frontend, backend, AI workflows, data systems, and product infrastructure.
Backend
Data Visualization
AI
Machine Learning
AI Agents
Automation
Databases
Deployment
Tools
Frontend
What's Next?
Let's build something together.
Actively building ML systems, LLM pipelines, and agentic AI — open to roles, internships, and meaningful collaborations.