Hi, I'm Aadhil
AI Engineer, Agentic AI & LLM Systems
Dubai, United Arab Emirates
I deliver enterprise AI solutions end to end, from architecture to deployment: multi-agent workflows, RAG pipelines, and the MLOps that keeps them running in production. I write here about the engineering behind it. More about me.
What I do
- Enterprise AI Solutions: End-to-end delivery, from architecture and system design to production deployment and monitoring.
- Agentic AI & Multi-Agent Systems: Designing agent workflows that reason, plan, and use tools reliably in production.
- RAG & LLM Applications: Building retrieval-grounded chatbots and enterprise document intelligence platforms.
- Conversational & Voice AI: Creating intelligent assistants and real-time voice agents with speech pipelines.
- LLM Engineering: Fine-tuning, evaluation, and cost and latency optimization for LLM workloads.
- MLOps & AI Infrastructure: Shipping containerized AI microservices with CI/CD, monitoring, and observability.
- Data Science & Analytics: Predictive modeling, A/B testing, and dashboards that drive business decisions.
Latest writing
All articles →Agentic Memory: How to Give AI Agents the Ability to Remember
A practical guide to agent memory: short-term, episodic, semantic, and procedural layers, with working Python implementations you can run today.
Simple Guide to MCP Authentication in Python with FastAPI
Learn how to build a secure MCP server using FastAPI with token-based authentication to enable AI agents to interact with your applications safely.
Efficient Data Encoding for Large Language Models
A practical look at TOON, a compact encoding format that cuts token usage when passing structured data to LLMs, with Python examples.
Benchmarking LLM Performance: Python vs Go
Real-world benchmark comparison of Python and Go clients for LLM APIs using Groq's ultra-fast inference service. Discover which language wins for speed and consistency.
