As a dedicated AI Engineer, I specialize in developing and deploying production-grade AI systems. My expertise spans Retrieval-Augmented Generation (RAG), Multi-Agent Architectures, and LLM Evaluation & Observability. I am passionate about leveraging cutting-edge AI to solve complex problems, particularly in the domains of Financial and Medical AI Assistants.
- Production-Grade AI Systems
- Retrieval-Augmented Generation (RAG)
- Multi-Agent Architectures
- LLM Evaluation & Observability (Langfuse, Ragas)
- Applied Research & Development
- Financial & Medical AI Assistants
- NLP & Generative AI Pipelines
- High-performance LLM inference (Groq Cloud)
- Continuous Learning & Growth
- Large Language Models: Fine-tuning & Optimization, Prompt Engineering, Evaluation Frameworks
- End-to-End AI Application Development
- ML & Deep Learning Frameworks
- AI-powered Chatbots & Assistants
- Cloud-Native Deployment & MLOps
I am actively seeking collaboration opportunities in:
- Enterprise AI Engineering
- RAG/LLMOps Product Development
- Open-source AI Tool Contributions
- Applied Machine Learning Research
My technical toolkit includes a diverse range of programming languages, AI/ML frameworks, and cloud technologies, enabling me to build robust and scalable AI solutions.
Here are some of my notable projects that showcase my expertise in building practical AI solutions:
| Project | Description | Tech Stack |
|---|---|---|
| 🏢 Enterprise RAG Platform | FastAPI service for multi-document RAG with OCR & embeddings | Python, FastAPI, LangChain, Qdrant, Docker |
| 🏥 Medical RAG Chatbot | Clinical assistant with FAISS retrieval, memory & Langfuse telemetry | Python, Flask, FAISS, OpenAI, Langfuse |
| 💹 Multi-Agent Financial Researcher | Phidata agents orchestrating finance/news workflows | Python, Phidata, Groq LLMs, YFinance |
| 🎌 AnimeGPT-LLMOps (Personal) | RAG recommender deployed with Docker + K8s + Grafana | Python, Streamlit, ChromaDB, Groq, Langfuse |
An overview of my GitHub activity and contributions:
My ongoing learning journey is centered around these key areas:
mindmap
root((Mahmoud's AI Journey))
LLMOps
RAG Systems
Langfuse + Ragas Evaluation
Prompt Engineering
Advanced ML
Deep Learning Architectures
Computer Vision
NLP Transformers
Generative AI
Professional Growth
Open Source Contributions
Industry Collaborations
Research Publications
Technical Leadership
</div>