“`
Live GenAI Engineering Program by TechieOps

Become Job-Ready in Generative AI Engineering

Learn how to build real AI systems using LLMs, Prompt Engineering, OpenAI API, RAG, LangChain, AutoGen, LangGraph, Claude, MCP, and enterprise AI safety practices.

✓ Build real GenAI projects
✓ Learn RAG and AI agents
✓ Interview and portfolio guidance
✓ Suitable for IT professionals and freshers
Take Admission Now View Full Syllabus

Next Live Batch Admission

Speak with TechieOps and confirm your seat for the upcoming Generative AI Engineering batch.

  • ✓ 8-week live online training
  • ✓ 7 structured GenAI modules
  • ✓ Module-wise hands-on projects
  • ✓ Final Virtual Analyst capstone
  • ✓ Interview and certification readiness
WhatsApp for Fees & Admission Email Course Advisor
Fast response for batch timing, fees, and enrollment process.
“`
8 Weeks Live online classes
7 Modules Complete GenAI roadmap
10+ Projects Hands-on learning
Capstone Virtual Analyst project
“`

Why Candidates Should Join This Program

This program is designed for learners who want to move beyond basic AI usage and understand how Generative AI systems are planned, built, evaluated, and delivered in real business environments. Candidates learn practical concepts like LLMs, RAG, agents, MCP, guardrails, and enterprise AI workflows through project-based training.

01

Build Real AI Systems

Create chatbots, summarizers, document Q&A assistants, agent workflows, and guardrail-enabled applications.

02

Learn Enterprise Tools

Work with OpenAI API, LangChain, RAG, AutoGen, LangGraph, Claude, MCP, and AI safety concepts.

03

Career-Focused Training

Prepare for interviews, project discussions, certification readiness, and GenAI-enabled job opportunities.

By the end of the program, candidates will be able to present a portfolio-ready GenAI capstone: a Virtual Analyst that analyzes documents, answers questions, generates insights, and uses agentic workflow concepts.

Who Is This Best For?

✓

IT Professionals

DevOps, Cloud, Data, QA, Support, and Software professionals who want to upgrade into GenAI roles.

✓

Developers and Architects

Professionals who want to design AI applications, RAG systems, and multi-agent workflows.

✓

Freshers and Students

Learners with basic technical knowledge who want practical project-based AI skills.

✓

Business and Automation Users

Managers, analysts, founders, and consultants who want AI productivity and automation skills.

What You Will Learn

LLM Foundations Tokenization Embeddings Prompt Engineering OpenAI API LangChain RAG AutoGen LangGraph Claude MCP Guardrails AI Safety AI Observability

Complete Generative AI Engineering Syllabus

The syllabus is structured into 7 practical modules with hands-on projects in every major topic.

Module 1 Foundations of LLMs and Prompt Engineering LLM basics, embeddings, structured prompts, OpenAI API +
  • Tokenization, embeddings, and language modeling concepts
  • How generative models interpret and generate text
  • Structured prompts using system, user, and assistant roles
  • Multi-turn dialogue, logic chaining, zero-shot, one-shot, and few-shot prompting
  • OpenAI API for summarization, classification, and transformation
Projects: Embeddings visualization, multi-turn chatbot, automated summarization, and sentiment-based email drafting.
Module 2 Building Systems with Generative AI Prompt pipelines, automation, response quality evaluation +
  • Convert real-world problems into structured prompt pipelines
  • Design multi-stage prompt execution for workflow automation
  • Evaluate model responses for safety, accuracy, and usefulness
Project: Customer support assistant with auto-response and retrieval logic.
Module 3 LangChain and Agent Systems LangChain, RAG, AutoGen, LangGraph +
  • LangChain fundamentals and chaining logic
  • Retrieval-Augmented Generation architecture
  • Designing private PDF-based Q&A assistants
  • AutoGen agent orchestration, coordinator-worker models, and role-based execution
  • LangGraph workflows with retry logic, failure handling, and human review loops
Projects: Private PDF Q&A assistant, three-agent customer support automation, and dynamic task-routing agent system.
Module 4 Reasoning with LLMs Advanced reasoning, critique loops, coding and multimodal tasks +
  • Advanced reasoning model behavior and applications
  • Critique loops and self-correction concepts
  • Applying reasoning in coding and multimodal tasks
Project: Debugging and multimodal question reasoning system.
Module 5 Enterprise Agent Use Cases and Anthropic Claude AutoGen advanced use cases, Claude, document reasoning +
  • Planner-executor architecture for enterprise automation
  • Tool integration, memory, feedback, and multi-model orchestration
  • Domain logic for HR, legal, technical, and operational workflows
  • Claude model family overview, multimodal design, and prompt caching
  • File-driven reasoning and tool-enabled execution
Projects: Resume screening and ranking agent system, plus AI assistant for document review and editing suggestions.
Module 6 Reasoning and Agent Project Sprint Integrated capstone using reasoning and agent collaboration +
  • Apply reasoning and agent collaboration in real systems
  • Combine LangGraph workflows with AutoGen autonomous logic
  • Prepare a working capstone architecture and demo flow
Capstone: Virtual Analyst that analyzes documents, answers queries, and generates insights.
Module 7 Memory, MCP, Guardrails and AI Safety Long-term memory, Model Context Protocol, safe AI delivery +
  • Persistent and evolving memory using LangGraph and AutoGen
  • Model Context Protocol standards for tool and app interoperability
  • MCP clients and servers
  • Validation, content filtering, and safe execution controls
  • AI delivery lifecycle, observability, versioning, feedback loops, and evaluation
  • Certification and interview readiness preparation
Projects: Adaptive agent with session-based memory, MCP-enabled multi-app ecosystem, and guardrail-enabled GenAI application.

Learning Outcomes

  • Understand LLM foundations, tokenization, embeddings, and model behavior
  • Write high-quality prompts for business, technical, and automation use cases
  • Build AI systems using prompt pipelines, OpenAI API, and structured workflows
  • Create RAG-based private document Q&A assistants
  • Design multi-agent systems using AutoGen and LangGraph concepts
  • Apply reasoning, memory, MCP, guardrails, and responsible AI practices
  • Explain your GenAI project confidently in interviews and professional discussions

Frequently Asked Questions

FAQ Do I need coding knowledge? Prerequisite clarity +
Basic programming knowledge is helpful for OpenAI API, LangChain, AutoGen, and LangGraph modules. The course starts from fundamentals and gradually moves toward advanced engineering concepts.
FAQ Will I build real projects? Hands-on learning +
Yes. You will build chatbots, summarizers, document Q&A systems, customer support assistants, multi-agent workflows, and a final Virtual Analyst capstone.
FAQ Is this useful for jobs and interviews? Career relevance +
Yes. The course is designed to help you explain practical GenAI systems, architecture, RAG, agents, MCP, guardrails, and capstone work during interviews and career discussions.
“`
Ready to start Generative AI Engineering? Talk to TechieOps and complete your admission process.
Enroll Now View Syllabus