Senior AI & Backend Engineer | LLMs & AI Agents | RAG | Cloud & DevOps
I build and deploy production-ready AI applications — combining LLMs, backend engineering, and cloud infrastructure to turn ideas into reliable systems.
- LLM-powered applications: Integrate and deploy models from OpenAI, Anthropic, and open-source ecosystems.
- AI agents & automation: Build tool-using agents, workflow automation, and AI voice experiences.
- RAG & document AI: Develop retrieval-augmented generation, document processing, and knowledge-based systems.
- Production AI infrastructure: Deploy, scale, monitor, and optimize AI workloads on cloud platforms.
- Backend & APIs: Build reliable services with Python, Django, Django REST Framework, and FastAPI.
- Cloud & DevOps: Automate infrastructure and delivery with AWS, Docker, Terraform, and CI/CD.
Building 10 production-grade DevOps projects — starting with system design, then implementing end-to-end on AWS / GCP / Azure using Terraform, Kubernetes, and CI/CD.
Follow the series on YouTube @devopsbymuh.
| # | Project | Stack | Repository |
|---|---|---|---|
| 1 | 3-Tier Web App on AWS | ECS Fargate · RDS Multi-AZ · Terraform · GitHub Actions | aws-3tier-devops-project |
⭐ New projects planned every 1–2 weeks.
- Building and deploying LLM-powered applications, AI agents, and AI automation workflows.
- Exploring practical LLM integration, model serving, inference, and cost-aware deployment patterns.
- Creating a 10-project production DevOps series: system design → implementation → deployment.
- Publishing hands-on tutorials on YouTube.
- Sharing technical articles on Medium and LinkedIn.
Focus areas: LLM APIs · Open-source LLMs · Prompt Engineering · RAG · Embeddings · Vector Search · AI Agents · Tool Calling · Model Serving · Inference Optimization · LLM Evaluation
AI deployment: Managed model endpoints · GPU workload orchestration · Containerized inference · CI/CD for AI applications · Cloud-based AI services
