Architecting production-grade AI systems and scalable backend infrastructure
class MuhammadTahir:
role = ["Data Scientist", "AI Engineer", "Backend Developer"]
experience = "2+ years in production AI/ML and backend systems"
focus = ["LLM Applications", "RAG Pipelines", "API-first Architecture"]
strengths = ["Scalable System Design", "Model Integration", "Data Engineering"]
currently = "Building intelligent backends that bridge AI reasoning and real-world scale"- Delivering end-to-end AI/ML solutions from prototype to production-grade deployment.
- Designing LLM-integrated services and retrieval-augmented systems grounded in domain knowledge.
- Engineering API-first backends with emphasis on reliability, observability, and performance.
- Bridging the gap between data science and software engineering in production environments.
| Domain | Expertise |
|---|---|
| AI / LLMs | RAG pipelines, LLM fine-tuning, prompt engineering, agent workflows |
| Backend | FastAPI microservices, async APIs, REST architecture, auth systems |
| Data Science | ML model development, feature engineering, experimentation at scale |
| Infrastructure | Docker containerization, service orchestration, production deployment |
| Project | Description | Stack |
|---|---|---|
| rag-chatbot | Production RAG pipeline with LLM-powered retrieval over domain documents | Python · LangChain · FastAPI |
| nexora-project | AI Agent framework for automated task orchestration | Python · OpenAI · Docker |
| Ecommerce_Django | Full-stack e-commerce platform with REST API backend | Django · PostgreSQL · HTML |
| Portfolio | Personal portfolio site — m-tahir.dev | TypeScript · Vercel |