Program Short Description
In this 12-week course, you will develop both the foundational concepts and the practical, hands-on technical skills needed to build and deploy Python-based Generative AI and Agentic AI applications. You will participate in a weekly, instructor-led live online class. Sessions are held every Saturday and run for approximately four hours.
By the end of the bootcamp, you will be expected to design and develop a Python Generative and Agentic AI application and deliver a live demonstration of your work to your cohort.
Contents include:
Python basic programming for Generative and Agentic AI development preparation.
Understand concepts for AI, LLM, Generative AI and Agentic AI.
Lean OpenAI Prompt engineering dashboard for trip planner, image generation, Code debugger and other tools.
AI Model, LLM, and Agent Testing
Review and use Open AI SDK for Python Generative AI Application Development.
Master Python Streamlit SDK for Python Web Application Development.
Build and deploy real-time generative AI-powered applications.
Want to Start Your IT Career
Python Generative and Agentic AI Development
Job Summary
As a Python Generative and Agentic AI Development Engineer, you will build and ship production-grade AI systems powered by large language models and agentic architectures that can reason, plan, and act. You’ll bridge research and real-world deployment—turning cutting-edge generative and autonomous-agent capabilities into secure, scalable products that deliver measurable value.
Role Overview
As a Python Generative and Agentic AI Development Engineer, you will play a pivotal role in developing next-generation AI solutions that leverage state-of-the-art generative models (LLMs, diffusion models, multimodal models) and agentic systems (autonomous agents, multi-agent coordination, tool-using workflows). You will collaborate closely with data science, ML engineering, product, and DevOps teams to design reliable AI applications, integrate third-party APIs and cloud infrastructure, and enforce best practices for testing, evaluation, and responsible AI.
What Do We Teach
The Python Generative and Agentic AI Engineering bootcamp program is crafted for both IT and non-IT professionals aiming to shift to a career as a Python AI Engineer.
In our training program, you will gain knowledge in Python programming, Agentic AI, and Generative AI concepts, along with practical coding skills for developing and deploying Python applications in Generative and Agentic AI.
Enrollment Requirement
You should have basic computer knowledge and be able to communicate with people in a basic English to understand the written lectures and do projects.
Class Time
North America Time Zone
USA New York Time
Monday - Friday (Self - Study, watch videos and do projects)
Every Saturday: 8:00 am - 12:00 pm (Instructor-led live online)
Europe Time Zone
Istanbul, Turkey Time
Monday - Friday (Self - Study, watch videos and do projects)
Every Saturday: 3:00 pm - 7:00 pm (Instructor-led live online)
Asia Time Zone
Tokyo, Japan Time
Monday - Friday (Self - Study, watch videos and do projects)
Every Saturday: 9:00 pm - 12:00 pm (Instructor-led live online)
Note: Daylight Saving time may be adjusted
Python Generative and Agentic AI Engineering
Bootcamp Training Content
Introduction to AI, LLM, and Models.
Introduction to Python Development
Python Development Environment Setup
Python Basics
Python OOP and Class
Python Data Structure
Python IO and JSON
Python Jupyter Notebook and Visual Studio Code
Prompt Engineering with Open AI Tools and Testing
AI Testing
OpenAI SDK for Python
Python Web Development with Streamlit
AI Agent Web Application Development
Building and Deploying Streamlit AI Applications.
Students Final Projects and Demo
Payment Method
We provide bank information for you, so you can send your tuition payment to the bank.
We do NOT accept cash payment.
Summer/Winter Break & Holidays
Two weeks of Summer/winter break will be scheduled either in July or August or December if the training program is held during the Summer or Winter season. Otherwise, there is no Summer/Winter break.
We will cancel classes when the class time overlaps with North America public holidays on Fridays.
Computer Requirements & Specification
In our training programs and projects, we use several online resources and software applications that must be installed on your computer. Your computer should meet the following specifications.
CPU: intel core i5 (six generations or newer) or equivalent
Operating System: Microsoft Windows 10 64-bit or newer;
Macbook Air or Macbook Pro
RAM (Memory): 8GB or above
Storage (Disk Size): 500 GB internal storage or 250 GB Solid State Driver (or newer)
Monitor: 15" above;
We require that you prepare an additional monitor so that you can watch the classroom lecture on one screen and do exercises on another monitor. Camera: the class is offered online using either the Zoom application or Google meet, so you should have a built-in computer camera or an external camera. Internet: the class is offered online, so you need a stable internet connection using either hardwired ethernet or wifi.
Computer brand: we do NOT require a specific computer brand; if your computer meets the above requirements, you can choose any computer brand.
Program Content Delivery
Instructor-led online hands-on training
Unlimited access to online videos and practical materials within one year
All training resources are available on the cloud.
Focus on small class sizes with maximum efficiency.
Consultants can attend the course online anywhere in the world
Program Certification & Award
United Coder Certificate Of Completion
How To Contact US
If you have any questions regarding the program, you can contact us with the following method.
Send email to services@unitedcoder.com
Assessment Site
Students are required to complete online quiz assessments and weekly coding projects every week. We will upload project requirements and quizzes on our Teachable platform.
Classroom Recordings Video Site
After each class, we will upload classroom recordings to our Teachable website, so students can watch classroom recordings the next day after the class.
Special Note
Our training program is rigorous, concentrating solely on concepts and technical skills to equip you for future careers in Python Generative and Agentic AI Engineering.
Besides participating in our four-hour live classes on Saturday, you are required to dedicate an additional 10 hours to watching videos, completing projects, and working on assignments either individually or in a group.
Each week, you will explore new concepts and complete assessments. We conduct live recordings of classroom content and discussions, allowing you to review the material by watching the recorded videos after classes. Therefore, we do NOT accept any registrations after the registration deadline. Thank you.
Future Career Development
After completing our Python Generative and Agentic AI Engineering bootcamp, you have the option to further your Python AI career through our upcoming programs or by enrolling in courses provided by other institutions.
Future Job Perspective (Career Outlook)
Strong and growing demand: As organizations productize generative AI, demand is rising for engineers who can move beyond prototypes to reliable, scalable, governed AI systems—especially those combining LLMs with tools, workflows, and enterprise integrations.
Shift from “prompting” to “agentic engineering”: The role is increasingly centered on building tool-using agents, workflow orchestration, and evaluation pipelines that make AI systems dependable under real-world conditions.
Deeper specialization opportunities: Career paths may branch into:
Agentic AI Architect / Staff Engineer (multi-agent design, orchestration, evaluation strategy)
LLM Platform Engineer (model serving, cost/latency optimization, internal APIs)
Applied Research Engineer (new agent patterns, alignment, multimodal systems)
AI Product & Solutions Lead (owning product outcomes, experimentation strategy, governance)
• • Long-term differentiators: Engineers who combine Python engineering excellence, evaluation/testing discipline, cloud deployment, and responsible AI practices will remain highly competitive as models commoditize and differentiation shifts to system design and operational quality.