1. AI-Enhanced MERN Stack
React, Node.js infused with OpenAI APIs
Smart React Apps
- AI Integration: Connecting React to OpenAI/Gemini APIs.
- Copilot: Speeding up frontend dev with AI coding assistants.
- Optimization: Using AI to analyze bundle size & performance.
- Next.js AI: Building Vercel AI SDK applications.
Intelligent Backends
- LangChain JS: Building reasoning chains in Node.js.
- Vector Search: Using MongoDB Atlas Vector Search.
- Chatbots: Building custom support bots with context memory.
- Security: Rate Limiting AI endpoints.
DevOps for AI
- CI Pipeline: Automated Testing via GitHub Actions. CI
- Docker: Containerizing AI Services for production.
- AWS Deployment: Hosting AI apps on EC2 & Lambda. Cloud
- Monitoring: Tracking Token Usage & Costs.
2. Generative AI & LLM Engineering
From Python Scripts to Autonomous AI Agents
Advanced Python
- FastAPI: Serving ML models as High-Speed APIs.
- Data Engineering: Pandas & NumPy for data prep.
- AsyncIO: Handling concurrent AI requests.
LLM Engineering
- Prompt Engineering: Mastering Few-Shot & Chain-of-Thought.
- RAG: "Chat with your PDF" using LangChain & Pinecone.
- Fine-Tuning: Customizing LLMs for business data.
MLOps & Azure AI
- Agent Deployment: Deploying Autonomous Agents.
- Azure AI: Using Azure OpenAI Service securely. Azure
- Evaluation: Testing LLM outputs (Ragas framework).
3. .NET Enterprise & AI
Building Intelligent Corporate Software
.NET Core & AI
- Semantic Kernel: Microsoft's SDK for AI orchestration.
- Azure AI Search: Implementing Hybrid Search in .NET.
- EF Core: Managing data for Enterprise AI apps.
Secure AI Arch
- IdentityServer: Securing AI endpoints.
- Microservices: AI services talking via RabbitMQ.
- Monitoring: Tracing AI calls with App Insights.
Azure DevOps
- Azure Pipelines: CI/CD for AI applications. CI/CD
- IaC: Deploying AI resources with Terraform.
- Serverless: Azure Functions for AI triggers.