Become a Job-Ready Python Full Stack Developer with AI
Learn to build complete web applications using Python, Django, FastAPI, React and databases — then add OpenAI-powered features, RAG systems, AI agents and production-ready workflows.
What You Will Learn?
A practical, project-led program covering Python full-stack development and AI application engineering.
What Does a Python Full Stack + AI Developer Build?
A Python Full Stack + AI Developer builds complete web applications with Python-based backends, modern frontend interfaces, databases and integrated AI capabilities. The role combines application engineering with the ability to connect language models, retrieval systems and agent workflows to real user needs.
The role combines application engineering with the ability to connect language models, retrieval systems and agent workflows to real user needs.
- Build responsive React interfaces and reusable components.
- Create Django applications and FastAPI services.
- Design database models and connect PostgreSQL or other suitable data stores.
- Integrate OpenAI APIs and selected Hugging Face models.
- Test, deploy and monitor full-stack applications.
- Deploying models with basic MLOps practices
Why This Role Is Exploding in 2026?
Applications That Understand Context
RAG connects a language model to approved documents or data so responses can use relevant context instead of relying only on model memory.
Workflows That Use Tools
AI agents can call approved tools, follow defined steps and route tasks. LangChain and LangGraph help structure those workflows.
Reliable AI in Production
Useful AI features need evaluation, logging, access controls, fallbacks and deployment practices — not just a successful demo.
Who Is This For?
Skills Covered in This Course
Python Programming
Efficient data manipulation for large datasets.
Django – FastAPI
Core supervised learning algorithms end-to-end.
React & Frontend Engineering
Create component-based interfaces, manage state, handle forms and connect frontend views to APIs.
Database Design
Model relational data, write SQL queries, use ORM patterns and understand indexing and pagination.
LLM API Integration
Connect OpenAI models, manage prompts and responses, and handle API errors, rate limits and usage.
RAG & Vector Retrieval
Chunk documents, create embeddings, retrieve relevant context and ground model responses.
Hugging Face Ecosystem
Explore model and dataset repositories, inference options, embeddings and open-source model workflows.
LangChain & LangGraph
Compose model calls and retrieval chains, then structure stateful, multi-step workflows as graphs.
AI Agents
Connect models to controlled tools, define workflow steps and account for failures or human review.
Mentorship Learning Model
Live Mentorship
Direct interaction with Mehul Prajapati during weekend live sessions on Zoom.
Hands-on Projects
Work on real-world AI applications, from RAG systems to multi-agent production apps.
Scenario-Based Learning
Solve enterprise-level problems using specific AI case studies and workflows.
AI Experiments
Compare prompts, retrieval results and model responses against clear task requirements.
Career Coaching
Optimize your LinkedIn, resume, and portfolio for the 2026 AI job market.
Exclusive Resources
Access proprietary prompt libraries, deployment scripts, and project templates.
Tools & Technologies You Will Master
Job Roles After Completing
Salary Potential in Python Stack + AI Careers (India)
Detailed Course Curriculum
A structured, fully visible syllabus — no hidden content.
Python & Development Fundamentals
- Python syntax, variables, data types, operators and control flow
- Lists, tuples, dictionaries, sets, comprehensions and string handling
- Functions, modules, packages, imports and virtual environments
- Object-oriented programming: classes, inheritance, encapsulation and composition
- File handling, exceptions, debugging and basic logging
- HTTP, JSON, command-line basics, Git and GitHub workflows
Frontend Development
- HTML structure, semantic elements, forms and accessibility fundamentals
- CSS layout, Flexbox, Grid, responsive design and reusable styling
- Modern JavaScript syntax, objects, arrays, modules and asynchronous programming
- React components, props, state, hooks and component composition
- Forms, validation, routing, API calls, loading states and error handling
- Connect a React interface to Python APIs and organize frontend project structure
Django, FastAPI & REST API
- Django project structure, apps, settings, URL routing, views and templates
- Django models, ORM, migrations, admin interface and authentication concepts
- Django REST Framework fundamentals for serializers, views, permissions and API endpoints
- FastAPI routes, request and response models, dependency injection and automatic API documentation
- REST conventions, HTTP methods, status codes, validation, error handling and logging
- When to use Django for full-featured web applications and FastAPI for focused API services
- API testing with Postman and automated tests for important endpoints
SQL, Databases & Data Modeling
- Relational database concepts, tables, keys, relationships and normalization basics
- SQL queries using SELECT, INSERT, UPDATE, DELETE, joins, grouping and subqueries
- PostgreSQL connection and configuration for Python applications
- Django ORM models, querysets, migrations and relationships
- Indexes, pagination, transaction basics and query-performance awareness
- Database credentials, environment variables and safe handling of application data
Full Stack Integration
- Connect React UI to Django or FastAPI endpoints
- Build registration, login, logout and role-aware application flows
- Understand sessions, tokens, JWT concepts, password hashing and permission checks
- Configure CORS, validate inputs and return consistent API errors
- Protect secrets with environment variables and separate development settings
- Recognize common web risks, including injection, insecure access and exposed credentials
- Build a full-stack CRUD application with a database-backed workflow
Generative AI
- Understand LLMs, tokens, context windows, inference and model limitations
- Prompt structure, system instructions, examples and structured output formats
- Call OpenAI APIs from Python and process model responses in application code
- Understand model parameters, structured outputs, tool/function calling and streaming concepts
- Integrate AI responses into a FastAPI or Django service and display them in a React interface
- Handle rate limits, API failures, usage controls, privacy and sensitive-data considerations
- Build a simple AI assistant with clear boundaries and helpful error states
Hugging Face & Open-Source Model Ecosystem
- Navigate the Hugging Face Model Hub, datasets and model cards
- Understand text generation, embeddings, classification and other model use cases
- Explore hosted inference options and using model APIs from Python
- Choose a model based on task quality, cost, latency, hardware and context requirements
- Use open-source models responsibly and check model licensing and usage constraints
- Compare model outputs against a small set of defined evaluation examples
Embeddings, Vector Databases & Retrieval-Augmented Generation (RAG)
- Understand RAG architecture, its use cases and limitations
- Ingest documents, extract text, split content into chunks and preserve metadata
- Generate embeddings and understand semantic similarity search
- Explore vector database concepts and retrieval configuration
- Retrieve relevant context and assemble it into a model prompt
- Build a document question-answering experience with grounded responses and source references
- Evaluate retrieval relevance, answer quality etc
LangChain & LLM Application Workflows
- Use prompt templates, model wrappers and output parsers
- Compose chains and reusable workflow steps
- Work with document loaders, text splitters and retrievers
- Connect tools and external Python functions to model-driven workflows
- Manage conversation history and understand memory design patterns
- Handle exceptions, timeouts, retries & model response validation
- Integrate a LangChain-powered workflow into a Python API
AI Agents & LangGraph
- Understand agent concepts, tools, planning and bounded decision loops
- Define LangGraph state, nodes, edges and conditional routing
- Build multi-step workflows with checkpoints, retries and clear exit conditions
- Use human-in-the-loop review for consequential actions
- Apply tool permissions, limits and failure-handling patterns
- Evaluate agent task completion, tool usage and reliability
- Build a task-oriented agent using approved tools and user confirmation where appropriate
AI & Agentic Full-Stack Applications
- Build an AI assistant inside a React application backed by Python services
- Create Django or FastAPI endpoints for model calls, retrieval and agent workflows
- Stream responses and manage conversation history and application state
- Build a document Q&A or knowledge assistant using RAG
- Create a task-oriented agent with approved tools, clear limits and user review
- Connect authentication, data persistence, frontend states and backend error handling
- Capstone project: deliver a complete Python + AI application with setup instructions and a project walkthrough
Deployment, Docker, Testing & MLOps
- Configure environments, dependencies, secrets and production settings
- Write and run tests for API endpoints, application logic and critical workflows
- Docker fundamentals and containerizing a Python application
- Understand deployment workflows, CI/CD concepts, version control and release practices
- Use logs and basic monitoring to investigate failures, latency and resource usage
- Track model versions, prompts, token usage and AI evaluation results where relevant
- Run regression checks against evaluation examples and understand basic observability
- Capstone: deploy a Python full-stack RAG or agentic application with documentation
Course Logistics
Build Your Python Stack with AI Career
Don't wait for the AI revolution to replace your skills. Lead it by becoming an AI Python Full Stack Developer.