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Read More →Uncodemy's 5–6 month instructor-led live online RAG Engineering program is open to learners in Madurai. Its syllabus takes in document ingestion and chunking, embeddings, vector databases, retrieval pipelines, hybrid search, and production RAG deployment. Created for developers, data scientists/ML engineers, and aspiring AI engineers, the program offers 13 modules, 90+ live sessions, 140+ hours, and an enterprise RAG capstone. Fees amount to ₹17,500 plus 18% GST, that is ₹20,650, and EMI starts from ₹4,032/month.
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Learners who complete the RAG Engineering training program at Uncodemy build practical skills in document processing, embeddings, vector databases, retrieval pipelines, and hybrid search. After their training and career preparation, eligible learners can apply for positions such as RAG Engineer, AI Application Engineer, and GenAI Engineer. Placement records for Madurai learners are updated regularly — view verified outcomes at uncodemy.com/placement.
RAG Engineering (Retrieval-Augmented Generation) is the art of combining large language models with external knowledge retrieval so that responses remain grounded in real, up-to-date enterprise data rather than leaning on the model's training data alone. It takes in document ingestion, chunking, embeddings, vector databases, and retrieval pipelines that push relevant context into an LLM at query time — cutting hallucination and enabling AI systems to answer questions about private, domain-specific, or rapidly evolving information.
What does a RAG engineer do? A RAG engineer handles document ingestion and chunking, builds embeddings and manages vector databases, designs retrieval pipelines, implements hybrid search, evaluates RAG accuracy, and deploys production RAG systems. Daily work involves Python scripting, working with frameworks like LangChain and LlamaIndex, tuning chunking and retrieval strategies, measuring relevance and faithfulness, and exposing RAG-powered APIs that applications can consume.
While RAG Engineering is about creating AI systems grounded in retrieval and enterprise knowledge, LLM Engineering takes a wider view of LLM-powered applications, and Generative AI highlights autonomous agents.
| Aspect | RAG Engineering | LLM Engineering | Generative AI |
|---|---|---|---|
| Core focus | Building retrieval-grounded AI systems on enterprise knowledge | Building, fine-tuning & deploying LLM-powered applications broadly | Building GenAI apps + autonomous AI agents |
| Coding depth | High | High | Moderate to High |
| Typical entry salary | ₹5–9 LPA (Entry-Level RAG Engineer) | ₹6–10 LPA (per LLM Engineering course sheet) | [CONFIRM per GenAI course sheet] |
| Uncodemy program duration | 5–6 months | 5–6 months | ~3–4 months (indicative) |
Salary ranges are indicative and vary by role, skills, experience, location and employer.
The RAG Engineering course is delivered as a 5–6 month live online program covering 90+ live sessions and 140+ hours of learning. Thirteen modules carry learners from RAG and enterprise knowledge AI foundations onwards through Python for RAG engineering, LLM fundamentals and API integration, document processing and data ingestion, text chunking and document transformation, embeddings and semantic search, vector databases and indexing, RAG, advanced retrieval and reranking, hybrid search and knowledge retrieval, RAG evaluation and optimization, production RAG APIs and deployment, and a 20-hour enterprise RAG capstone. Each module features practical exercises with real tools, and the program ends with hands-on RAG projects and an enterprise capstone. It is delivered fully live online for Madurai and nationwide learners.
Who should join this course:
Eligibility: Programming fundamentals are helpful but not mandatory — Python is taught from Module 2 onward. Familiarity with basic LLM concepts is a plus but built up in Module 3. A laptop with stable internet is required for practical sessions.
Select a batch timing that matches your routine. Live online batches are available in IST timings. Book a free demo class to get a first-hand feel, or sign up for our upcoming weekend and weekday live batches — tailored for both working professionals and students, with hands-on training and flexible timings.
| Date | Time | Trainer | Seats Left |
|---|---|---|---|
| 16oct | 07:30 PM – 08:30 PM | Mr. Irshad | 01Seat |
| 22oct | 02:00 PM – 03:00 PM | Mr. Upendra Kumar Tiwari | 02Seats |
| 26oct | 10:30 AM – 11:30 AM | Mr. Irshad | 01Seat |
| 30oct | 12:00 PM – 01:00 PM | Mr. Upendra Kumar Tiwari | 02Seats |
| Date | Time | Trainer | Seats Left |
|---|---|---|---|
| 27oct | 03:00 PM – 05:00 PM | Mr. Irshad | 02Seats |
8+ new batches start every month. All timings are in IST. Seats are limited and updated daily.
The syllabus is organised into 13 modules covering 140 hours. The order follows how RAG engineering actually works: foundations → Python → LLM fundamentals & API integration → document processing & chunking → embeddings & semantic search → vector databases → RAG → advanced retrieval/reranking → hybrid search → evaluation → production deployment → capstone. Each module includes practical exercises with real tools, and the program closes with hands-on RAG projects and an enterprise RAG capstone.
| # | Module | Hours | Tools |
|---|---|---|---|
| 1 | RAG & Enterprise Knowledge AI Foundations | 8 | Python, LLM APIs |
| 2 | Python for RAG Engineering | 10 | Python, Git |
| 3 | LLM Fundamentals & API Integration | 10 | OpenAI APIs, Hugging Face |
| 4 | Document Processing & Data Ingestion | 8 | Python, LlamaIndex |
| 5 | Text Chunking & Document Transformation | 8 | LangChain, LlamaIndex |
| 6 | Embeddings & Semantic Search | 10 | Hugging Face, OpenAI Embeddings |
| 7 | Vector Databases & Indexing | 10 | FAISS, ChromaDB, Pinecone, pgvector |
| 8 | Retrieval-Augmented Generation (RAG) | 12 | LangChain, LlamaIndex, Vector DBs |
| 9 | Advanced Retrieval & Reranking | 10 | LangChain, Cross-Encoders |
| 10 | Hybrid Search & Knowledge Retrieval | 10 | Elasticsearch/OpenSearch, LangChain |
| 11 | RAG Evaluation, Accuracy & Optimization | 12 | RAGAS, LangSmith, Python |
| 12 | Production RAG, APIs & Deployment | 12 | FastAPI, Docker, Cloud Deployment Tools |
| 13 | Enterprise RAG Projects & Capstone | 20 | All tools |
| Total | 140 Hours | 15+ tools | |
Once all 13 modules, the assignments, and the Enterprise RAG capstone project are completed, you are granted the Uncodemy RAG Engineering Program Certificate. The certificate documents the program name, duration, tools covered, and completion date. Verifiable online at certificate.uncodemy.com, it serves as a record of successful completion of the Uncodemy RAG Engineering program.
The certificate is tied to the practical work completed during the program — including hands-on RAG pipeline work, retrieval evaluation and the 20-hour Enterprise RAG capstone project. Learners work with Python, OpenAI APIs, Hugging Face, LangChain, LlamaIndex, LangGraph, FAISS, ChromaDB, Pinecone, PostgreSQL/pgvector, Elasticsearch/OpenSearch, FastAPI, Docker, Git/GitHub and cloud deployment tools.
This is an independent Uncodemy certificate. It is not affiliated with, endorsed by, or a credential of OpenAI, Pinecone or any other RAG/vendor provider whose tools are taught in this course. Certificate verification process pending confirmation.
Uncodemy Certificate Verification Online Certificate Verification & Download certificate.uncodemy.com_2.webp)
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OpenAI APIs
LangChain
LlamaIndex
Vector Databases
Hugging Face
FAISS
ChromaDB
Pinecone
FastAPI
Docker
<Elasticsearch
Git & GitHub
Operated by Uncodemy Edutech Pvt. Ltd., Uncodemy is a career-focused IT training institute. With over 14 years of training excellence, classroom centres in Delhi and Noida, and live online classes that reach learners across India, it provides instructor-led training in emerging technologies.
In this RAG Engineering program, learners build retrieval pipelines, hybrid search systems, production RAG APIs and complete an enterprise RAG capstone project. The training covers how retrieval-grounded AI systems are engineered in production — document processing, embeddings, vector search, retrieval evaluation and deployment. You can read the full module-by-module curriculum before you enrol.
Alongside training, Uncodemy provides instructor-led sessions, practical assignments, project-based learning, interview preparation and career support. What that support includes — and what it does not — is set out in our career support policy, along with the eligibility conditions published before enrolment rather than after payment.
If you are still deciding on a direction, Uncodemy also runs LLM Engineering, AI Testing and Data Science courses for Madurai and Maharashtra learners.
Portfolio/capstone project review:Your hands-on RAG pipelines, retrieval evaluation work and enterprise RAG capstone are reviewed and refined into a portfolio an interview will find credible.
Resume & LinkedIn:We build your resume around RAG engineering keywords and measurable outcomes, and align your LinkedIn profile to match.
Interview preparation:Retrieval pipeline design questions, chunking-strategy trade-offs, evaluation-metric discussions and RAG engineering case studies specific to AI hiring.
Mock interviews:Sessions with industry trainers, followed by written feedback on what to fix.
Opportunity sharing:Relevant openings from our hiring network of 850+ companies are shared with you, with support through the application and interview process.
Continued support:This is active assistance, not a guarantee of employment — no training institute can honestly promise a job. Support continues after your program ends for learners who stay engaged with the process.
The people who actually teach this course
Irshad Khan, Senior Trainer at Uncodemy, leads this program, bringing 8+ years across Data Science, Data Analytics, SQL, Power BI, Python and Generative AI. He's also the one who reviewed and approved the curriculum you see here.
Most institutes blur two jobs into one; Uncodemy keeps them apart. Trainers handle the live teaching. A separate group of domain reviewers — specialists who never teach the batch — check that the curriculum stays accurate and up to date. Upendra Kumar Tiwari reviews the technical content for this program, so the person teaching it isn't the only one vouching for it.
Uncodemy publishes every trainer and domain reviewer by name, along with their area of focus and background — so you can check them out before enrolling, not afterward.
Senior Data Science Trainer
Expertise15+ years of industry experience building production machine learning systems for retail, telecom and consulting teams.
Modelling security breaches in iOS apps, dormant customer prediction and factor analysis, last-mile delivery optimisation, and credit card fraud detection.
Data Analytics & AI Trainer
ExpertisePython · SQL · Machine Learning · Tableau · Artificial Intelligence · Deep Learning · Power BI · MLOps
8+ years across consulting and public-sector analytics, with deep hands-on work in AI and MLOps delivery.
Built an OCR model, a disease-prediction model, and a fraud-control model deployed across every Indian state under the Ministry of Health & Welfare.
Data Analytics & AI Trainer
ExpertiseExcel, SQL, Python, statistics, Power BI, Tableau and Generative AI
7+ years turning aviation and enterprise data into forecasting models, dashboards and AI assistants.
AI agent chat/voice bot, AI-driven data analysis, a sales-forecasting ML model, NPS data model evaluation, and a face-recognition model for biometrics.
Data Analytics Trainer
ExpertisePython · SQL · Excel · Power BI · Tableau · ML · DL · NLP · Time Series · IoT Devices · Prompt Engineering · Chatbots
5+ years in logistics and consumer-brand analytics, with a strong focus on time series, NLP and applied automation.
Speech-to-text using core NLP (T2S), talent acquisition research, and product launch & CMS work on keventers.com using Sparktech.
Data Analytics Trainer
ExpertiseAdvanced Excel · SQL · Power BI · Tableau · Python · Data Visualisation · Statistics
Trainer for the analytics and business intelligence track at Uncodemy, working with learners on Excel, SQL and BI tooling from first principles to interview-ready projects.
Guides classroom capstones on sales and customer dashboards, KPI reporting and end-to-end data cleaning workflows.
Data Analytics Trainer
Expertise
Data Analytics Trainer
Expertise
MD & CEO
ExpertiseFounder, MD & CEO of Uncodemy and GS Net Academy, with experience in education, technology leadership, research, mentorship and career development.
Leads strategic direction, academic initiatives, technology-driven education programs and mentorship across Uncodemy and GS Net Academy.
Data Analytics Trainer
Expertise
MD & CEO
ExpertiseFounder, MD & CEO of Uncodemy and GS Net Academy, with experience in education, technology leadership, research, mentorship and career development.
Leads strategic direction, academic initiatives, technology-driven education programs and mentorship across Uncodemy and GS Net Academy.
CEO & Founder
ExpertiseCEO & Founder of GvCloud Secure and Uncodemy with 14+ years of professional experience building scalable web, mobile and AI-driven solutions for clients across India, USA, UK, Canada, Australia and Singapore.
Leads technology strategy, scalable web and mobile solutions, AI-driven products and EdTech initiatives across Uncodemy and GvCloud Secure.
Sign up for Uncodemy's RAG Engineering course designed for Madurai learners today! Build hands-on expertise in document processing, embeddings, vector databases, retrieval pipelines and hybrid search via instructor-led live online sessions, practical projects and an enterprise RAG capstone. Begin your RAG engineering journey with organised training and career readiness.
| Role | Core Tools |
|---|---|
| RAG Engineer | LangChain, LlamaIndex, Vector DBs, Python |
| AI Application Engineer | LangChain, FastAPI, Vector DBs |
| GenAI Engineer | OpenAI APIs, LangChain, Embeddings |
| LLM/RAG Engineer | LangChain, LlamaIndex, Evaluation Tools |
| AI Solutions Engineer (Junior) | FastAPI, Docker, Cloud Deployment Tools |
| Search/Retrieval Engineer | Elasticsearch/OpenSearch, Vector DBs, Python |
| Role | Salary Range |
|---|---|
| Entry-Level RAG Engineer | ₹5–9 LPA |
| RAG / GenAI Engineer | ₹8–15 LPA |
| AI Application Engineer | ₹8–16 LPA |
| LLM / RAG Engineer | ₹10–20 LPA |
| Senior RAG Engineer | ₹18–30 LPA |
| AI Solutions Engineer | ₹15–28 LPA |
| Lead AI / RAG Engineer | ₹25–40 LPA |
| Factor | This Program |
|---|---|
| Duration | 5–6 months |
| Learning hours | 140+ |
| Live sessions | 90+ |
| Tools | 15+ |
| Capstone | 20-hour Enterprise RAG project |
| Career support | Dedicated Career & Placement Assistance |
| If you want... | Recommend |
|---|---|
| Full RAG Engineering career transition | This program |
| Already know LLM Engineering fundamentals and want deeper retrieval expertise | This program |
| Building AI agents specifically | Consider Generative AI & Agentic AI course |
| Broad LLM application development | Consider LLM Engineering course |
This course is structured to run for 5–6 months and offers 140+ hours of learning through 90+ live sessions. Learners may pick either weekday or weekend batches based on their convenience.
Having basic programming knowledge is useful, though it is not compulsory. Python is taught as part of the curriculum, and foundational LLM concepts are developed during the training. A dependable laptop and internet connection are expected throughout the course.
The program covers over 15 tools and platforms, including Python, OpenAI APIs, Hugging Face, LangChain, LlamaIndex, LangGraph, FAISS, ChromaDB, Pinecone, PostgreSQL/pgvector, Elasticsearch/OpenSearch, FastAPI, Docker, Git/GitHub, and commonly used cloud deployment tools.
Yes. The curriculum features dedicated modules on vector databases and indexing, followed by hybrid search and knowledge retrieval. These sessions are practical and meant to help you work with retrieval systems in real projects.
The capstone is a structured end-to-end project that combines document processing, chunking, embeddings, vector search, retrieval logic, evaluation, and deployment. It is designed to mirror how a real enterprise RAG system is built and tested.
The sessions are conducted live by instructors, and all classes are recorded so learners can revisit them for revision and practice.
The course fee is ₹17,500 plus 18% GST, making the total ₹20,650. The same fee structure applies across the available learning modes and locations.
EMI options are available, with monthly instalments starting from around ₹4,032 depending on the applicable plan. The exact tenure and eligibility are confirmed by the admissions team based on the selected payment option.
Yes. A demo class is available so you can review the teaching approach, understand the curriculum, and ask questions before enrolling.
The program includes dedicated career support such as resume guidance, LinkedIn optimization, portfolio review, mock interviews, and opportunity sharing through our network of 850+ companies. It supports your job search, but it does not guarantee employment.
Entry-level RAG engineering roles in the market typically fall in the range of ₹5–9 LPA, but actual results depend on your background, project work, interview performance, and employer. Uncodemy does not promise a fixed salary or job outcome.
Indicative market ranges are around ₹5–9 LPA for entry-level roles, ₹8–15 LPA for mid-level RAG and GenAI roles, and higher for senior positions. Actual salary offers vary by employer, skill level, and market conditions, and Uncodemy does not guarantee any particular salary.
Graduates can target roles such as RAG Engineer, AI Application Engineer, GenAI Engineer, LLM/RAG Engineer, AI Solutions Engineer (Junior), and Search/Retrieval Engineer.
This course is suited to developers looking to specialize in retrieval-based AI systems, data professionals moving into GenAI and RAG work, aspiring AI engineers, and working professionals preparing for RAG-focused roles.
The figures shown on this page are based on Uncodemy's internal records and publicly available review sources. They provide a market reference, but learner outcomes still depend on skill level, experience, location, employer, and market conditions. Career support is advisory and does not guarantee a job or fixed salary.
The course is delivered live online for learners across India, including Madurai. In-person classroom support is available at our Delhi and Noida centres for other programs, while Madurai learners generally attend through the live online format.
No. The certificate is an independent Uncodemy credential and is not affiliated with, endorsed by, or issued by OpenAI, Pinecone, or any other vendor whose tools are taught in the course.
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