Aniket Bedwal

Class Location

Online class via Zoom

Student's Home

Tutor's Home

Years of Experience in Data Science Classes

3

Class Location

Online class via Zoom

Student's Home

Tutor's Home

Years of Experience in Generative AI Classes

3

Teaching Experience in detail in Generative AI Classes

I have 3 years of professional experience in AI/ML engineering, with a strong focus on Generative AI and production-grade AI applications. My teaching approach is based on what I build and use in real-world projects rather than only theoretical concepts. I teach Generative AI from fundamentals to advanced implementation, including LLMs, prompt engineering, embeddings, vector databases, RAG, advanced RAG, LangChain, LangGraph, Agentic AI, multi-agent systems, tool calling, MCP, evaluation, and AI observability. Students learn by building practical projects and understanding how GenAI systems work end-to-end, from designing the architecture and choosing models to retrieval, orchestration, APIs, evaluation, and production deployment. I also focus on helping students understand why and when to use different GenAI techniques, common production challenges, and how modern AI engineering is done in the industry. The sessions are adapted to the student's current level, whether they are starting with Generative AI, preparing for AI engineering interviews, or looking to build production-ready GenAI applications.

Class Location

Online class via Zoom

Student's Home

Tutor's Home

Years of Experience in Full Stack Agentic AI

3

Teaching Experience in detail in Full Stack Agentic AI

I have 3 years of hands-on industry experience building production-grade AI systems, with a strong focus on Full Stack Agentic AI, Generative AI, and LLM applications. I teach students how to build complete AI products from concept to production, combining AI engineering with backend development and deployment. My Full Stack Agentic AI curriculum covers Python, FastAPI, LLMs, Prompt Engineering, LangChain, LangGraph, AI Agents, Multi-Agent Systems, ReAct Agents, Tool Calling, Function Calling, MCP (Model Context Protocol), Agent Memory, Context Engineering, RAG, Advanced RAG, Embeddings, Vector Databases, Semantic Search, Hybrid Search, Reranking, Structured Outputs, Guardrails, and Human-in-the-Loop workflows. Students also learn production concepts including REST APIs, PostgreSQL, Redis, Docker, AWS/Azure, asynchronous processing, LLM evaluation, RAGAS, LangSmith, observability, latency and cost optimization, and production deployment. My teaching is project-driven. Students build end-to-end Agentic AI applications where agents can reason, retrieve information, use external APIs and tools, maintain state and memory, and execute multi-step workflows. The goal is to develop practical Full Stack AI Engineering skills that can be applied to real-world projects, AI Engineer interviews, and production applications.

Class Location

Online class via Zoom

Student's Home

Tutor's Home

Years of Experience in Full stack generative AI

3

Teaching Experience in detail in Full stack generative AI

I have 3 years of professional experience in AI/ML engineering, with a strong focus on Generative AI and production-grade AI applications. My teaching approach is based on what I build and use in real-world projects rather than only theoretical concepts. I teach Generative AI from fundamentals to advanced implementation, including LLMs, prompt engineering, embeddings, vector databases, RAG, advanced RAG, LangChain, LangGraph, Agentic AI, multi-agent systems, tool calling, MCP, evaluation, and AI observability. Students learn by building practical projects and understanding how GenAI systems work end-to-end, from designing the architecture and choosing models to retrieval, orchestration, APIs, evaluation, and production deployment. I also focus on helping students understand why and when to use different GenAI techniques, common production challenges, and how modern AI engineering is done in the industry. The sessions are adapted to the student's current level, whether they are starting with Generative AI, preparing for AI engineering interviews, or looking to build production-ready GenAI applications.

Class Location

Online class via Zoom

Student's Home

Tutor's Home

Years of Experience in AI Training

3

Teaching Experience in detail in AI Training

I have 3 years of professional experience in AI/ML engineering, with a strong focus on Generative AI and production-grade AI applications. My teaching approach is based on what I build and use in real-world projects rather than only theoretical concepts. I teach Generative AI from fundamentals to advanced implementation, including LLMs, prompt engineering, embeddings, vector databases, RAG, advanced RAG, LangChain, LangGraph, Agentic AI, multi-agent systems, tool calling, MCP, evaluation, and AI observability. Students learn by building practical projects and understanding how GenAI systems work end-to-end, from designing the architecture and choosing models to retrieval, orchestration, APIs, evaluation, and production deployment. I also focus on helping students understand why and when to use different GenAI techniques, common production challenges, and how modern AI engineering is done in the industry. The sessions are adapted to the student's current level, whether they are starting with Generative AI, preparing for AI engineering interviews, or looking to build production-ready GenAI applications.