I'm an AI Engineer specializing in agentic AI, multi-agent systems, retrieval-augmented generation, and knowledge graphs. I enjoy turning early AI prototypes into reliable, user-facing products that can reason over complex information, remember context, use external tools, and automate real business workflows.

My work spans the full AI application stack: agent orchestration, long-term memory, RAG and GraphRAG, vector databases, backend APIs, workflow automation, observability, and production deployment.

• 🎓 Bachelor of Computer Science in Artificial Intelligence from Swinburne University of Technology
• 🤖 Experienced in designing multi-agent LLM workflows and tool-using AI agents
• 🧠 Built persistent memory and personalized knowledge systems for AI applications
• 🔎 Developed hybrid retrieval pipelines with RAG, GraphRAG, vector search, and knowledge graphs
• ⚙️ Built backend services and automated workflows that connect AI agents with external systems
• 📊 Implemented observability for production AI systems using MLflow, Datadog, and PostHog

Dubai, UAE | February 2025 – June 2026

• Helped architect and scale a production-grade agentic AI platform from an early prototype into a stable user-facing deployment.
• Designed multi-agent LLM workflows with LangChain, including orchestration, tool calling, prompt engineering, and workflow coordination.
• Developed persistent user memory with the Letta Agents SDK to retain preferences, conversation history, and long-term context.
• Built user-centric knowledge graphs with Neo4j and FalkorDB for contextual reasoning and personalization.
• Developed hybrid retrieval pipelines using RAG, GraphRAG, Qdrant, and FAISS.
• Automated end-to-end workflows with n8n, external APIs, databases, and business systems.
• Implemented AI observability with MLflow, Datadog, and PostHog to improve reliability and troubleshoot production issues.

Dubai, UAE | August 2024 – December 2024

• Developed a retrieval-augmented generation solution for HR screening.
• Improved candidate information retrieval by researching and optimizing document chunking strategies.
• Presented technical findings and recommendations to the engineering team.

• Multi-agent systems that coordinate specialized AI agents
• AI assistants with persistent, personalized memory
• RAG and GraphRAG pipelines grounded in reliable source data
• Knowledge graph applications that connect users, documents, and relationships
• Production APIs and automated workflows for AI-powered products
• Observable AI systems that are easier to monitor, evaluate, and improve

I'm interested in opportunities and collaborations involving agentic AI, multi-agent platforms, RAG, GraphRAG, knowledge graphs, and production AI engineering.

• 🌐 Portfolio
• 💼 LinkedIn
• 💻 GitHub
• 💻 LeetCode