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Online Classes English Proficient
JNTU 2004
Bachelor of Technology (B.Tech.)
Nagole, Hyderabad, India - 500068
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Class Location
Online class via Zoom
Student's Home
Tutor's Home
Years of Experience in Data Science Classes
8
Data science techniques
Machine learning, Python, Artificial Intelligence
Class Location
Online class via Zoom
Student's Home
Tutor's Home
Years of Experience in Full Stack Agentic AI
8
Teaching Experience in detail in Full Stack Agentic AI
I have hands-on experience training professionals and aspiring AI engineers in Full Stack Agentic AI, focusing on building production-oriented AI applications from the agent layer through backend APIs and deployment. My teaching experience includes: Agentic AI fundamentals: Agent architecture, LLM-powered applications, tool/function calling, ReAct patterns, planning, reasoning and multi-agent systems. Agent development: Building practical agents using LangGraph, LangChain, LlamaIndex, CrewAI and AutoGen, including planner–executor–verifier and multi-agent workflows. RAG and Knowledge Systems: Designing end-to-end RAG pipelines using embeddings, vector databases, retrieval, re-ranking and contextual generation. Backend development: Teaching how to expose AI agents through Python/FastAPI APIs, structure production-grade applications, integrate tools and manage API workflows. Full-stack AI application development: Connecting agentic backends with application interfaces, APIs, databases, authentication, business logic and AI workflows to create complete usable solutions. MCP and tool integration: Practical understanding of connecting AI agents with external tools, services and data sources through MCP and tool-calling patterns. LLMOps and production practices: Introducing evaluation, observability, prompt management, model monitoring, CI/CD and responsible AI practices using tools such as LangSmith and MLflow. Hands-on labs and projects: Conducting practical demonstrations and guided implementation rather than focusing only on theory, enabling learners to build working Agentic AI applications.
Answered on 20/04/2024
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Class Location
Online class via Zoom
Student's Home
Tutor's Home
Years of Experience in Data Science Classes
8
Data science techniques
Machine learning, Python, Artificial Intelligence
Class Location
Online class via Zoom
Student's Home
Tutor's Home
Years of Experience in Full Stack Agentic AI
8
Teaching Experience in detail in Full Stack Agentic AI
I have hands-on experience training professionals and aspiring AI engineers in Full Stack Agentic AI, focusing on building production-oriented AI applications from the agent layer through backend APIs and deployment. My teaching experience includes: Agentic AI fundamentals: Agent architecture, LLM-powered applications, tool/function calling, ReAct patterns, planning, reasoning and multi-agent systems. Agent development: Building practical agents using LangGraph, LangChain, LlamaIndex, CrewAI and AutoGen, including planner–executor–verifier and multi-agent workflows. RAG and Knowledge Systems: Designing end-to-end RAG pipelines using embeddings, vector databases, retrieval, re-ranking and contextual generation. Backend development: Teaching how to expose AI agents through Python/FastAPI APIs, structure production-grade applications, integrate tools and manage API workflows. Full-stack AI application development: Connecting agentic backends with application interfaces, APIs, databases, authentication, business logic and AI workflows to create complete usable solutions. MCP and tool integration: Practical understanding of connecting AI agents with external tools, services and data sources through MCP and tool-calling patterns. LLMOps and production practices: Introducing evaluation, observability, prompt management, model monitoring, CI/CD and responsible AI practices using tools such as LangSmith and MLflow. Hands-on labs and projects: Conducting practical demonstrations and guided implementation rather than focusing only on theory, enabling learners to build working Agentic AI applications.
Answered on 20/04/2024
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