AI-Powered Artificial Intelligence Course & Internship in Chennai
Master modern Generative AI and LLM application development with a structured 130-day learning path covering Python, MySQL, Generative AI, Transformers, Prompt Engineering, RAG, Fine-Tuning, FastAPI, LangChain, LangGraph and an AI Capstone Project at UNIQ Technologies.
Why Choose an Artificial Intelligence with Python Course & Internship in Chennai?
Looking for an Artificial Intelligence with Python course in Chennai that goes beyond basic Python programming? The AI with Python program at UNIQ Technologies is designed for students, freshers, aspiring AI developers and software professionals who want to build practical knowledge in Python, Generative AI, Transformers, Prompt Engineering, RAG, Fine-Tuning, FastAPI, LangChain, LangGraph and AI application development.
From Python Fundamentals to Autonomous AI Agents
AI developers construct context-aware GenAI systems, retrieval-augmented generation pipelines, and agent workflows using RAG, LangChain and FastAPI.
Python Programming
Master Python fundamentals, OOP, data structures, file handling and AI library integration.
MySQL Database
Execute SELECT queries, table relationships, subqueries, and manage structured application databases.
Generative AI & LLMs
Understand Large Language Model concepts, model interaction, prompt completion and GenAI use cases.
Transformer Architecture
Learn self-attention mechanisms, encoder-decoder models, and transformer language foundations.
Prompt Engineering
Design role-based prompts, structured JSON outputs, context framing, constraints and prompt optimization.
RAG (Retrieval Augmented Generation)
Connect external knowledge sources, vector search embeddings, and retrieve document context for LLMs.
Fine-Tuning
Customize pre-trained models using domain-specific dataset adaptation and instruction tuning workflows.
FastAPI REST Serving
Build high-performance async REST API endpoints to serve AI predictions and streaming responses.
LangChain Framework
Build multi-step LLM chains, prompt templates, memory modules and tool integration.
LangGraph Agent Workflows
Design stateful multi-agent workflows, cyclic graphs, autonomous agents and decision-making flowcharts.
AI Tools
Master developer tooling for testing prompts, API debugging, vector storage and environment management.
AI Capstone Project
Build a complete production-grade AI application combining Python, RAG, FastAPI and LangChain/LangGraph in 20 days.
Program Objective
Provide a practical pathway from Python programming to modern Generative AI agent workflows, retrieval-augmented generation, and production API serving.
Who Can Join Artificial Intelligence Training?
The program starts with core fundamentals and moves towards full stack development—designed for anyone eager to build a career in software development.
BE / B.Tech Students & Graduates
Engineering graduates looking to build careers in Generative AI and LLM Application Engineering.
BCA & MCA Graduates
Computer application graduates ready for practical Python, LangChain, RAG and FastAPI skills.
B.Sc & M.Sc Graduates
Science postgraduates interested in modern AI architectures, language models and prompt engineering.
Python Developers
Developers expanding into Generative AI APIs, vector search, RAG pipelines and LangGraph agents.
Software Developers
Developers adding AI capabilities, intelligent workflows, and custom LLM interfaces to existing systems.
Aspiring AI Developers
Learners focused on building context-aware AI applications and custom agentic software.
Final-Year Students & Freshers
Students seeking hands-on project experience before entering IT recruitment for AI roles.
Career Switchers
Professionals looking to transition into high-growth Artificial Intelligence and GenAI opportunities.
Artificial Intelligence Roadmap
A structured learning path that moves from fundamentals and backend frameworks to AI, cloud concepts, professional tools and job preparation.
Learning Path
Course Modules
Python Programming
Python forms the programming foundation of the AI course. Approximately 35 days are allocated to Python so learners master programming before moving into advanced AI.
Key Topics
What you'll learn
Python Fundamentals
Variables, data types, operators, conditionals and loops
Data Structures
Lists, tuples, sets, dictionaries, string processing
Functions & OOP
Functions, modules, packages, classes, objects and inheritance
File & Exception Handling
Working with files, exception handling and modular code
Learning Focus
A strong Python foundation is essential when integrating AI libraries, APIs, and LangChain frameworks.
A structured path. No rushed learning.
The learning program distributes time across programming, databases, backend development, AI, professional tools, and career preparation.
Overall Program
The duration is intentionally spread across the complete learning journey instead of compressing the syllabus into a short-term crash course.
Python Programming
ProgrammingMySQL Database
DatabaseGenerative AI & LLMs
GenAI CoreTransformers
ArchitecturePrompt Engineering
Model SteeringRAG (Retrieval Augmented Generation)
RAG & SearchFine-Tuning
Model AdaptationFastAPI
API ServingLangChain & LangGraph
AI FrameworksAI Tools
Developer ToolsAI Capstone Project
Capstone ProjectCore programming receives one of the largest portions of the learning timeline.
Approximate days across frontend, core programming, databases, and web foundations.
Overall program duration covering technical and career preparation.
What Skills Can You Develop During the Full Stack Internship?
Progress through the syllabus and understand how the major parts of a modern application connect—from the user interface to backend services and databases.
Programming & Database
Build a strong foundation in Python OOP, file processing, and MySQL data management.
GenAI & Model Architecture
Understand Large Language Models, Transformer attention mechanisms, and fine-tuning.
RAG & Agent Frameworks
Build context-aware RAG pipelines and autonomous multi-agent workflows.
Supporting skills for modern development
The core application stack is supplemented by exposure to modern development practices, cloud technologies, AI and software delivery tools.
Practical Artificial Intelligence Training for Freshers
Freshers often face a particular problem when applying for their first software job: employers expect practical understanding even when candidates have no professional experience. An internship-oriented training environment helps bridge this gap.
At UNIQ Technologies, the emphasis is on learning concepts through coding and practical implementation rather than depending exclusively on theory.
Students should be able to explain:
Essential topics for fresher technical interviews
How to build a Retrieval Augmented Generation (RAG) pipeline
Chunking documents, generating vector embeddings, and retrieving relevant context for LLMs.
How to design role-based and structured JSON prompts
Structuring prompt instructions, constraints, and system messages to enforce JSON response schemas.
How LangChain connects LLMs with external tools
Building multi-step prompt chains, memory buffers, and binding external Python functions as tools.
How LangGraph manages stateful multi-agent workflows
Constructing state graphs, conditional routing nodes, and autonomous agent loops.
How to serve AI models with high-performance FastAPI endpoints
Exposing asynchronous prediction and streaming API endpoints to serve AI applications.
How Transformer attention mechanisms process token context
Understanding self-attention, positional encoding, and how context windows expand LLM reasoning.
How to organize structured data in MySQL for AI applications
Writing SQL queries to store conversation histories, user settings, and application logs.
How to build an end-to-end AI project pipeline
Combining Python backend, RAG vector retrieval, LangChain workflows, and FastAPI streaming.
Full Stack Projects That Connect the Full Stack
Project-based learning is an important part of becoming comfortable with Full Stack development. A practical project helps students understand how individual technologies work together as one complete application.
Project Architecture
One application. Multiple technologies. One workflow.
Data & Storage
Python + MySQL
Manage user profiles, application history, and relational data.
RAG Retrieval
Vector Embeddings + RAG
Perform similarity search on document chunks and pass context to LLMs.
Prompt Steering
Structured Prompts
Design system instructions, role personas, and JSON output constraints.
Agentic Workflows
LangChain + LangGraph
Orchestrate multi-step chains, tool invocation, and stateful agent graphs.
API Serving
FastAPI Endpoints
Expose async REST streaming endpoints to connect front-end AI interfaces.
Artificial Intelligence Career Opportunities
Depending on skills, project knowledge, interview performance, and employer requirements, learners can explore various entry-level opportunities across the software industry.
AI Developer
Junior AI Developer
Generative AI Developer
AI Application Developer
Python AI Developer
Python Developer
AI Engineer – Entry Level
Machine Learning / AI Trainee
Backend Developer with AI Skills
Software Developer – AI Applications
Frequently Asked Questions
Everything you need to know about the internship, learning path, technologies, practical training and career preparation.
An AI with Python course teaches Python programming alongside Artificial Intelligence and modern AI application-development concepts.
UNIQ Technologies offers an AI with Python program covering Python, MySQL, Generative AI, Transformers, Prompt Engineering, RAG, Fine-Tuning, FastAPI, LangChain, LangGraph and an AI project.
The total course duration is 130 days.
Yes. Python is the first major module and is allocated 35 days.
Python provides a widely used programming foundation for AI-related development and is supported by a broad ecosystem of libraries and tools.
Yes. MySQL is covered for approximately 15 days.
Yes. Generative AI is a major component and is allocated 20 days.
The syllabus focuses strongly on modern Generative AI and AI application development, while also providing the Python foundation required to work with AI technologies.
Yes. Transformers are covered for approximately 10 days.
Transformers are foundational to many modern language-model systems. Understanding their concepts helps learners understand how contemporary Generative AI systems work.
Prompt Engineering involves designing effective instructions and context for AI models to generate useful and structured outputs.
Yes. The course includes 5 days of Prompt Engineering.
RAG stands for Retrieval Augmented Generation. It enables an AI application to retrieve relevant information and provide that information as context to a generative model.
Yes. RAG is included for approximately 5 days.
Yes. The syllabus includes approximately 5 days of Fine-Tuning.
Fine-Tuning involves adapting an existing model using task- or domain-specific data to improve its suitability for a particular use case.
Yes. FastAPI is included for approximately 5 days.
FastAPI can be used to expose Python-based functionality through APIs, making it useful when connecting AI functionality with web or software applications.
Yes. LangChain is included together with LangGraph.
LangChain is commonly used to help developers structure applications that interact with language models and connect models with tools, data and application workflows.
LangGraph is used to model and manage more structured, stateful or multi-step AI application workflows.
They provide learners with exposure to frameworks and patterns used for developing structured LLM-based applications and autonomous agents.
Yes. 20 days are allocated to an AI Capstone Project.
A project allows learners to combine multiple concepts and understand how AI technologies work together inside a real software application.
Yes. The course is designed for freshers interested in Python and Artificial Intelligence who are willing to build their programming foundation first.
Yes. Beginners can start with Python and progressively move toward Generative AI and AI application development.
Prior professional Python experience is not required. The course itself provides 35 days of foundational Python training.
Yes. BCA students interested in AI and Python development can consider this learning path.
Yes. Engineering students can learn Python and progress into Generative AI and AI application development.
Yes. MCA students can use the program to strengthen their Python and AI application-development skills.
Students from different educational backgrounds can learn AI, starting with Python to build their programming foundation.
AI becomes progressively more complex as learners move into topics such as Transformers, RAG and Fine-Tuning. A structured learning path starting with Python makes the progression manageable.
For beginners, learning Python fundamentals first is essential. This syllabus allocates 35 days to Python before the Generative AI module.
Generative AI is a subfield within Artificial Intelligence that focuses specifically on systems capable of generating content based on learned patterns.
Artificial Intelligence is the broad umbrella field covering intelligent systems. Generative AI specifically focuses on creating text, images, code and other outputs.
RAG provides models with external information at inference time without modifying weights, while Fine-Tuning changes model behavior using additional training data. They solve different needs.
Not necessarily for every Generative AI application development pathway. Understanding Python, APIs, and GenAI concepts provides a strong foundation for building AI apps.
The course includes FastAPI, AI frameworks, and a 20-day project, giving learners hands-on exposure to building AI applications.
Yes. Python can be used with frameworks like FastAPI to create APIs that connect AI functionality to web or mobile interfaces.
Yes. The syllabus specifically allocates 20 days to the AI Capstone Project.
Possible projects include AI assistants, document-based question-answering RAG systems, AI-powered APIs, and agentic workflows.
The syllabus covers broader Generative AI concepts, Prompt Engineering, RAG, Transformers, Fine-Tuning and frameworks like LangChain/LangGraph.
Yes. There is a dedicated 5-day Tools module.
Possible roles include AI Developer, Python Developer, Generative AI Developer, AI Application Developer, AI Trainee and Python AI Developer.
Yes. Python development provides an ideal programming foundation for learning Generative AI application development.
A certificate demonstrates completion, but AI roles generally require practical skills, programming ability, project experience and technical understanding.
Freshers should prepare Python, Generative AI concepts, Transformers, Prompt Engineering, RAG, APIs, and be able to explain their project architecture.
Compare the Python foundation, Generative AI coverage, RAG, Transformers, Prompt Engineering, Fine-Tuning, API development, AI frameworks and project work.
Yes. The supplied syllabus specifies a total course duration of 130 days.
Interested students can contact UNIQ Technologies through internshipinchennai.com to check batch schedules, eligibility and admission details.