A Job-Ready Java Full Stack Developer with AI
Build complete web applications with Core Java, Spring Boot, React and databases — then add intelligent features using Spring AI, LLM APIs, RAG, AI agents and production-ready workflows.
Core Java & OOP
Master object-oriented programming, collections, exception handling and modern Java features.
What Does a Java Full Stack + AI Developer Build?
A Java Full Stack + AI Developer builds web applications with Java-based backend services, modern frontend interfaces and databases, then integrates AI capabilities such as language-model responses, document retrieval and tool-assisted workflows.
Java and Spring Boot are widely used in enterprise application environments where maintainability, security and integration are important.
- Build responsive React interfaces and reusable components.
- Create Spring Boot services and REST APIs.
- Design database models and persist data using SQL, JPA and Hibernate.
- Integrate Spring AI or suitable Java libraries with LLM APIs.
- Build RAG chat experiences and controlled, tool-using AI workflows.
- Test, secure, package and deploy full-stack applications.
Why This Role Is Exploding in 2026?
Enterprise-Ready Foundations
Java and Spring Boot support structured applications with clear layers, integration patterns and mature testing practices.
Knowledge Connected to AI
RAG can help an application answer questions using approved documents or internal knowledge sources with relevant context.
Responsible AI Integration
Useful AI features require access controls, evaluation, logging, fallbacks and human review where appropriate.
Who Is This For?
Skills Covered in This Course
Core Java & OOP
Work with classes, interfaces, collections, generics, exceptions, streams and reusable code.
Spring Boot
Build layered applications with controllers, services, repositories, configuration and dependency injection.
React
Create component-based interfaces, handle forms and connect views to backend APIs.
SQL, JPA & Hibernate
Model relational data, write SQL, map entities and manage persistence safely.
REST API Engineering
Design endpoints, validate requests, return consistent errors and document API contracts.
Spring Security
Understand authentication, authorization, role-based access and secure configuration.
LLM Integration
Connect Java applications to model APIs and manage prompts, responses, errors and usage.
RAG & Vector Retrieval
Process documents, create embeddings, retrieve relevant context and ground model responses.
AI Agents & Tool Calling
Connect models to approved functions and structure workflows with limits and review steps.
Testing & Quality
Test service logic, REST endpoints and integration paths with JUnit and suitable testing tools.
Docker & Deployment
Understand application packaging, environment configuration and CI/CD fundamentals.
AI Evaluation & Monitoring
Track output quality, failure cases, latency and usage in AI-enabled applications.
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.
1-on-1 Feedback
Get personalized review on your code and architecture designs via Discord.
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 AI Careers (India)
Detailed Course Curriculum
A structured, fully visible syllabus — no hidden content.
Core Java & Programming Fundamentals
- Java syntax, variables, primitive and reference types, operators and control flow
- Methods, arrays, strings, packages and access modifiers
- Object-oriented programming: classes, objects, inheritance, abstraction, encapsulation and polymorphism
- Interfaces, composition, generics and common design principles
- Collections Framework, exception handling, file I/O and debugging
- Modern Java features, lambda expressions, streams and Optional
- JDK, JVM, build tools, Git and GitHub fundamentals
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 React interfaces to Spring Boot APIs and organize frontend project structure
Spring Boot, Spring MVC & REST AP
- Spring ecosystem, project setup, dependency injection and inversion of control
- Spring Boot configuration, profiles, application properties and layered architecture
- Controllers, services, repositories, DTOs and request/response mapping
- REST conventions, HTTP methods, status codes, validation and exception handling
- Spring Data JPA repositories and API documentation fundamentals
- Pagination, sorting, filtering and consistent API response patterns
- API testing with Postman and automated tests for important endpoints
SQL, Databases, JPA & Hibernate
- Relational database concepts, tables, keys, relationships and normalization basics
- SQL queries using SELECT, INSERT, UPDATE, DELETE, joins, grouping and subqueries
- PostgreSQL or MySQL configuration and connectivity with Spring Boot
- JPA entities, repositories, relationships, mapping and Hibernate fundamentals
- Transactions, lazy and eager loading concepts, pagination and query performance
- Indexes, schema migrations and safe handling of database credentials
Full Stack Integration, Authentication & Security
- Connect React UI to Spring Boot endpoints and manage frontend state
- Build registration, login-logout & role-aware app flows
- Understand Spring Security, password hashing, authentication and authorization
- Explore session-based authentication and token/JWT concepts as appropriate to the project
Generative AI Foundations & Spring AI
- Understand LLMs, tokens, context windows, inference and model limitations
- Prompt structure, system instructions, examples and structured output formats
- Connect a supported model provider through Spring AI or a suitable Java client library
- Use chat clients, prompt templates, response handling and streaming concepts where supported
- Understand model options, structured outputs, tool/function calling and multimodal possibilities
- Integrate AI responses into a Spring Boot 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
LLM APIs, Model Choices & AI Application Patterns
- Understand hosted model APIs and compare providers by task, latency, cost and context needs
- Manage prompts, response formats, model parameters and configuration
- Use structured outputs and validate model-generated data before application use
- Explore embeddings and model capabilities through supported Java libraries or APIs
- Apply timeouts, retries, fallback behaviour and rate-limit handling
- Check provider terms, model limitations, data handling and privacy requirements
- Evaluate output quality against defined examples rather than relying on a single successful demo
Embeddings, Vector Stores & RAG
- Understand RAG architecture, use cases and limitations
- Ingest documents, extract text, split content into chunks and preserve metadata
- Generate embeddings and understand semantic similarity search
- Explore vector store concepts and supported integrations through Spring AI or suitable libraries
- Retrieve relevant context and assemble it into a model prompt
AI Workflow Orchestration
- Understand when to use a deterministic workflow versus a model-directed workflow
- Connect AI model calls to approved Java methods and external APIs
- Define tool schemas, validate inputs and check permissions before executing actions
- Compose reusable prompt, retrieval and application service steps
- Manage conversation context and persistence deliberately
- Handle exceptions, timeouts, retries, output validation and safe fallbacks
AI Agents, Multi-Step Workflows & Human Review
- Understand agent concepts, tools, planning and bounded decision loops
- Design multi-step workflows with explicit state, transitions and completion conditions
- Connect agents to approved tools and restrict high-impact actions
- Use human-in-the-loop review for consequential actions
- Apply tool permissions, guardrails, step limits and failure-handling patterns
- Evaluate task completion, tool selection, reliability and unintended behaviour
AI & Agentic Full-Stack Applications
- Build an AI assistant inside a React application backed by Spring Boot
- Create Java services for model calls, retrieval and tool workflows
- Stream responses and manage conversation history and application state
- Build a document Q&A or internal knowledge assistant using RAG
- Create a task-oriented workflow with approved tools, clear limits and user review
- Connect authentication, data persistence, frontend states and backend error handling
Testing, Docker, Deployment & MLOps Fundamentals
- Write unit and integration tests with JUnit, Mockito and Spring testing support
- Test API endpoints, service logic, data access and critical AI workflows
- Configure environments, dependencies, secrets and production settings
- Docker fundamentals and containerizing a Spring Boot application
- Understand deployment workflows, CI/CD concepts, version control and release practices
Course Logistics
Build Your Java Stack + AI Career
Don't wait for the AI revolution to replace your skills. Lead it by becoming an AI Java Stack Application Developer.