Design, build, and deploy production-grade AI systems. Work with LLM applications, RAG pipelines, and enterprise AI architectures through hands-on labs.
AI Technologies Used By Leading Companies
The 12-Week AI Engineering & Generative AI Program prepares you for real engineering roles in the rapidly growing field of artificial intelligence. Instead of focusing only on theory, the program emphasizes building, deploying, and operating AI systems similar to those used in modern technology companies.
Students learn how to develop machine learning models, integrate Large Language Models (LLMs), build AI APIs, and deploy intelligent applications capable of solving real-world business problems.
During the program, you will work inside simulated production environments where AI systems fail due to data corruption, model drift, hallucinations, or performance issues. You will diagnose these failures and restore the system — gaining practical experience that mirrors real AI engineering work.
By the end of the program, you will have built complete AI systems, including chatbots, recommendation engines, and enterprise AI assistants, giving you the skills needed to transition into AI engineering roles.
This program is designed for beginners who want to start their journey in Artificial Intelligence and AI application development. No prior AI experience is required.
₹ 69,999
Early Enrollment Benefit Applied
Python
LLM APIs
LangChain
Docker
Vector DB
FastAPI
Build production-grade AI systems and graduate with a portfolio that proves your engineering capability.
Build a conversational AI assistant capable of answering customer queries using LLM APIs and structured prompts.
Create an AI system capable of reading company documents and answering questions using RAG pipelines.
Train and deploy machine learning models capable of detecting suspicious financial transactions.
Develop a recommendation system that suggests products based on user behavior and purchase patterns.
Build an AI backend capable of generating marketing content and reports using LLM APIs.
Design and deploy a full enterprise AI assistant for answering internal company questions.
Hands-on labs simulating real AI system failures including data issues, model drift, hallucinations, and production deployment problems.
A customer analytics AI system fails because corrupted data enters the training pipeline. Diagnose the issue and rebuild a clean dataset pipeline.
A fraud detection system suddenly drops from 92% accuracy to 60%. Investigate model drift and retrain the model.
An e-commerce AI system incorrectly classifies product images, causing incorrect listings on the website.
An internal AI assistant gives incorrect answers because the LLM lacks access to company documentation.
A company wants to automate customer support using AI. Build and deploy an intelligent chatbot.
A machine learning model works locally but fails in production. Diagnose deployment issues and restore service.
Design and deploy a production AI assistant capable of answering questions from company documents.
Real-world AI failures engineers face in production systems. Students diagnose and fix enterprise AI incidents.
Fraud detection AI model accuracy drops due to data drift.
AI chatbot response time increases during peak traffic.
AI assistant cannot retrieve documents due to corrupted embedding index.
Diagnose and stabilize a failing enterprise AI assistant experiencing hallucinations and latency.
In 12 structured weeks, evolve from learning AI fundamentals to building and operating real AI systems used in modern companies.
Python, data processing, machine learning fundamentals.
Train models, build chatbots, and integrate LLM applications.
Deploy AI APIs, monitor models, and troubleshoot production failures.
Build intelligent applications using machine learning, generative AI, and production AI pipelines.
Train, optimize, and deploy machine learning models that power data-driven applications.
Build AI chatbots, knowledge assistants, and LLM-powered applications used by modern companies.
You will build real AI systems, simulate production failures, and graduate with practical experience used in modern AI teams.
The 8-Week AI Fast Track is designed for developers and technical professionals who want to quickly transition into AI engineering roles. This program focuses on building real AI applications using modern tools such as LLM APIs, Retrieval Augmented Generation (RAG), and AI backend systems.
Instead of theoretical machine learning lectures, you will work on production-style AI projects including AI assistants, document intelligence systems, and AI-powered APIs. By the end of the program, you will have built multiple AI systems and gained hands-on experience with the technologies used in modern AI products.
This accelerated program is designed for developers and engineers who already have basic programming knowledge and want to quickly transition into AI engineering roles.
₹ 49,999
Limited Seats Available
LLM Systems
LangChain
Docker
Vector DB
FastAPI
Build practical AI applications used in modern products, from intelligent chatbots to document assistants and AI APIs.
Build a conversational AI assistant capable of answering customer queries using LLM APIs and structured prompts.
Create an AI system capable of reading company documents and answering questions using Retrieval Augmented Generation.
Develop an AI backend capable of generating marketing content, summaries, and reports using LLM APIs.
Build an intelligent search assistant that retrieves information from company knowledge bases.
Develop a recommendation system that suggests products based on user activity and purchase patterns.
Deploy an AI assistant capable of answering internal employee queries using company documentation.
Accelerated real-world scenarios focused on building and deploying practical AI applications.
An AI assistant generates inconsistent responses because poorly structured prompts are used in production.
A document assistant fails to retrieve relevant information from company documentation.
A marketing team needs an AI service capable of generating product descriptions automatically.
An e-commerce platform needs a recommendation engine to suggest products based on user activity.
Deploy a company knowledge assistant capable of answering internal employee questions using enterprise documents.
Real-world AI system failures designed to simulate production issues faced by AI engineers.
Users report that the AI assistant generates incorrect or hallucinated responses.
RAG system becomes slow during heavy query traffic from enterprise users.
AI application crashes during peak traffic due to LLM API rate limits.
AI service fails after deployment due to API configuration errors.
Company AI knowledge assistant stops responding during peak internal usage.
In 8 accelerated weeks, transition from experimenting with AI tools to designing and deploying real AI-powered applications.
Create chatbots, AI APIs, and intelligent assistants.
Expose AI models through APIs and production services.
Design scalable AI services used in modern software products.
Build intelligent applications powered by LLM APIs, chatbots, and AI automation systems.
Design Retrieval-Augmented Generation systems, prompt pipelines, and enterprise AI assistants.
Deploy scalable AI APIs and integrate AI capabilities into real production applications.
Designed for developers who want to rapidly transition into AI engineering and build real AI products.
The 4-Week AI Specialization is designed for engineers who want to deepen their expertise in production AI systems and enterprise AI architectures. This program focuses on advanced topics such as scaling AI services, optimizing RAG pipelines, and handling real-world AI system failures.
You will operate inside simulated enterprise environments where AI systems experience real production challenges such as latency issues, hallucination debugging, API scaling failures, and knowledge retrieval problems. The goal is to train engineers who can design, stabilize, and operate AI systems used in real companies.
This specialization is intended for engineers already working with AI tools, machine learning systems, or backend development who want to master production AI system design and optimization.
₹ 34,999
Limited Elite Cohort
LLM Debugging
LangChain
AI Deployment
Vector DB
Operate and improve production-grade AI systems used in real companies. Focus on reliability, scaling, and enterprise AI architecture.
Design an enterprise AI assistant capable of answering internal company questions using multi-document RAG pipelines.
Build an AI system capable of retrieving information from multiple knowledge sources including documents, APIs and databases.
Develop a scalable AI API service capable of handling high traffic workloads and enterprise integrations.
Build a system that validates AI responses and reduces hallucination risks using context validation pipelines.
Design a deployment workflow for production AI systems including model serving, monitoring and API scaling.
Deploy a complete enterprise AI assistant capable of handling internal employee queries with optimized retrieval.
High-stakes incident scenarios designed for engineers operating real production environments.
Users report that the AI assistant is generating incorrect responses. Diagnose prompt design and improve context retrieval to reduce hallucinations.
Enterprise knowledge assistant fails to retrieve relevant documents from the vector database. Diagnose embedding and retrieval pipeline issues.
AI application crashes during high user traffic due to LLM API rate limits and poor request handling.
Internal company AI assistant stops responding during peak usage. Diagnose the full AI pipeline and restore service.
High-pressure real-world AI system failures designed to simulate production incidents faced by enterprise AI engineers.
An enterprise AI assistant begins generating incorrect answers, causing internal teams to rely on false information.
An AI API serving thousands of users experiences severe latency spikes during peak traffic.
An enterprise AI assistant fails to retrieve relevant company documents due to vector database misconfiguration.
A production fraud detection model suddenly drops in prediction accuracy due to real-world data drift.
In 4 intensive weeks, move beyond building models and learn how to diagnose, stabilize, and optimize real AI systems running in production.
Analyze model drift, hallucinations, and broken AI pipelines.
Debug embeddings, RAG pipelines, and LLM response failures.
Improve AI reliability, performance, and enterprise deployment.
Diagnose and stabilize production AI systems used in enterprise applications.
Design scalable AI architectures including RAG pipelines, vector databases, and LLM APIs.
Monitor AI models, detect drift, and ensure high-performance AI services in production.
Designed for engineers who already build AI systems and want to operate, debug, and scale production AI infrastructure.
Whether you're starting your AI journey or upgrading your engineering skills, this program provides hands-on experience with real AI systems.
Build a strong foundation in AI engineering by developing real AI applications and portfolio-ready projects.
Upgrade your engineering skills by learning how to build LLM applications, AI APIs, and intelligent systems.
Expand your data skillset by learning how to deploy machine learning models and AI-powered applications.
Enable teams to build and deploy AI-powered products and enterprise AI solutions.
AI Engineer Transformation
Beginner → Advanced
Flexible Installments Available
Applied AI Engineering
Intermediate → Advanced
Limited Seats Per Cohort
Enterprise AI Systems
Advanced Engineers Only
Strict Prerequisites Required
Flexible training models designed for individuals, institutions, and enterprise teams.
Instructor-led or self-paced programs with 24×7 access to real-world labs.
Structured classroom programs for colleges, universities, and training institutes.
Customized cybersecurity programs for enterprise teams and organizations.
No prior AI experience is required for the 12-week program. The training starts with Python fundamentals and core AI concepts before moving into LLM applications, RAG systems, and enterprise AI deployment.
You will build real AI systems such as AI chatbots, document intelligence assistants, recommendation engines, and enterprise knowledge assistants. These projects simulate real-world AI applications used in modern companies.
Yes. The program includes hands-on work with modern AI tools including OpenAI APIs, LangChain, vector databases, embeddings, and RAG pipelines used to build production AI systems.
All labs are practical and project-based. You will build and deploy AI applications, design AI APIs, and work with real datasets to simulate production AI systems used by companies.
Yes. The program is designed for both students and working professionals who want to transition into AI engineering or upgrade their skills in modern AI application development.
Yes. Upon successful completion of the training and capstone project, you will receive an industry-recognized certificate from Eduvoxy validating your AI engineering skills.
Yes. The program includes mentor guidance, project feedback, resume building, and interview preparation to help learners transition into AI-related roles.
You can enroll or request a demo by using the Enroll option on this page or by emailing us at contact@eduvoxy.com.
After completing the program and portfolio projects, learners can pursue roles such as AI Engineer, LLM Application Developer, Machine Learning Engineer, AI Product Developer, and Data Scientist. The program focuses on building practical AI systems that align with modern industry requirements.
Most AI courses focus heavily on theory or academic machine learning. This program focuses on building real-world AI systems such as chatbots, RAG-based knowledge assistants, AI APIs, and enterprise AI applications using modern tools like OpenAI, LangChain, and vector databases.
No. Most AI applications in this program use cloud-based AI APIs and hosted services. A standard laptop capable of running Python and web development tools is sufficient to complete the training and projects.
Yes. Throughout the program you will build multiple AI applications including chatbots, AI assistants, recommendation engines, and enterprise knowledge systems. These projects can be included in your professional portfolio or GitHub to demonstrate your skills to employers.
Yes. The training is designed to help learners transition into AI engineering roles by focusing on practical development skills, real-world AI projects, and industry tools used by modern AI teams. The program also includes career guidance and interview preparation.
Enroll in the AI Career Transformation program to access all production labs, modules, and certification track.
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Learn with production-grade labs, real scenarios, and structured career support.