Course Description

In this hands-on technical course, learners will build a fully functional AI-powered mock interviewer using CrewAI and Streamlit. Starting with a recap of agent-based systems, the course guides learners through designing multi-agent architectures, integrating speech-to-text and text-to-speech components, and deploying a web-based chatbot interface. By implementing CrewAI for role-based task assignment and simulating realistic interview scenarios, learners will gain practical experience in LLM integration, autonomous agent collaboration, and end-to-end app development. Ideal for those looking to explore agentic AI systems and real-world applications in career readiness and edtech.

Course curriculum

  • 1
    Course Introduction
    • Course Overview
    • Recap of CrewAI
    • Project Introduction
    • Course Handouts
    • Quiz
  • 2
    Understanding the Agent System Architecture
    • High-level System Architecture and Components
    • Understanding the Components
    • Implementing Speech-to-text
    • Implementing App using Streamlit
    • Implementing Text-to-Speech (Bonus Feature)
    • Quiz
  • 3
    Implementing AI-Powered Mock Interviewer
    • Architecture
    • Project Implementation- Part I - Building the Crews
    • Project Implementation- Part II - Building the CLI Functionality-
    • Project Implementation_ Part III - Introduction to the WebUI Chatbot
    • Project Implementation_ Part IV - Streamlit Implementation and STT
    • Course Conclusion
    • Advanced Assessment– Now Open for Booking!

Who Should Enroll

  • AI enthusiasts exploring agentic systems

  • Developers building LLM-based apps

  • Educators and edtech innovators

  • Job seekers practicing with mock interviews

Key Takeaways

  • How to design and implement a multi-agent system using CrewAI for real-world applications

  • Ways to integrate speech-to-text and text-to-speech features to create interactive AI experiences.

  • Step-by-step development of a mock interview chatbot using both CLI and Streamlit UI.

  • Best practices for structuring agent roles, assigning tasks, and managing workflows in AI-driven apps

About the Instructors

Alessandro Romano - Senior Data Scientist, Kuehne+Nagel

Alessandro Romano is a Senior Data Scientist at Kuehne + Nagel and an accomplished public speaker. With over five years of experience in data analysis, he brings deep technical expertise in implementing large language model (LLM) - based solutions across diverse industries.
About the Instructors

FAQ

  • Do I need prior experience with CrewAI to take this course?

    Not at all. The course begins with a recap of CrewAI fundamentals, making it accessible even if you're new to the platform.

  • Will I build a complete mock interviewer application?

    Yes! By the end of the course, you’ll have a fully functional AI-powered mock interviewer with both CLI and Streamlit interfaces.

  • What technologies will I work with in this course?

    You’ll use CrewAI, OpenAI’s language models, Python, Streamlit, and speech processing tools like STT and TTS.

  • Can I use this project in my portfolio or for job prep?

    Absolutely. The project is highly relevant for edtech, career readiness tools, and demonstrating LLM integration skills.