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RAG Engineering Training Course in Chhatrapati Sambhajinagar (Aurangabad) by Uncodemy

Q: What is the duration of the RAG Engineering course in Chhatrapati Sambhajinagar (Aurangabad)?

The program runs for 5–6 months and includes 140+ hours of learning across 90+ live sessions. Weekday and weekend batches are both available.

Q: Do I need prior Python or LLM experience?

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.

Q: What tools are covered in this course?

15+ tools including Python, OpenAI APIs, Hugging Face, LangChain, LlamaIndex, LangGraph, FAISS, ChromaDB, Pinecone, PostgreSQL/pgvector, Elasticsearch/OpenSearch, FastAPI, Docker, Git/GitHub and cloud deployment tools.

Q: Does the course cover vector databases and hybrid search?

Yes. Module 7 covers Vector Databases & Indexing (10 hours) and Module 10 covers Hybrid Search & Knowledge Retrieval (10 hours). Both are core components with hands-on exercises.

Q: What is the capstone project?

The capstone is Module 13: Enterprise RAG Projects & Capstone (20 hours). You build an end-to-end RAG application applying document processing, embeddings, vector search, retrieval, evaluation and deployment.

Q: Are the classes live or pre-recorded?

All 90+ sessions are live and instructor-led, with recordings available afterwards for revision.

Q: What is the fee for the RAG Engineering course in Chhatrapati Sambhajinagar (Aurangabad)?

The program fee is ₹17,500 plus 18% GST. Total fee is ₹20,650. The same fee applies in all modes and cities.

Q: Are EMI options available?

EMI starts from ₹4,032 per month. The exact amount, tenure and eligibility depend on the applicable payment or financing option — a counsellor can confirm your plan.

Q: Is there a demo or trial class before I enrol?

Yes. You can attend a demo session to see the teaching style, meet the trainer and review the curriculum before deciding. Contact us to book a demo.

Q: Do you provide placement assistance?

We provide Dedicated Career & Placement Assistance — resume building, LinkedIn optimisation, portfolio review, interview preparation, mock interviews and opportunity sharing through our network of 850+ companies. We do not guarantee employment.

Learners in Chhatrapati Sambhajinagar (Aurangabad) can join Uncodemy's 5–6 month instructor-led live online RAG Engineering program. The course covers document ingestion & chunking, embeddings, vector databases, retrieval pipelines, hybrid search and production RAG deployment. It is aimed at developers, data scientists/ML engineers and aspiring AI engineers, and includes 13 modules, 90+ live sessions, 140+ hours and an enterprise RAG capstone. Fee: ₹17,500 + 18% GST, total ₹20,650. EMI starting from ₹4,032/month.

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Lifetime Access

Get lifetime access to recorded sessions & materials

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Resume Building, Mock Interviews & Job Support

Certified Course

Certificate of Completion from Uncodemy

Flexible Batches

Weekday & Weekend Online & Offline

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Uncodemy?

14+

Years of Training Excellence

850+

Companies in Our Hiring Network

54,000+

Learners Trained

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Program Details

RAG Engineering Course — Live Online Program Hands-on learning focused on practical RAG engineering skills and interview preparation.

A comprehensive 5–6 month instructor-led live online program covering embeddings, vector databases, retrieval pipelines, hybrid search, and production RAG deployment with practical projects, mentoring and career support.

📚 Learning:

140+ Hours of Learning

⏱️ Program Duration:

5–6 Months

🎓 Live Training:

90+ Live Sessions

🛠️ Technology Coverage:

15+ Tools & Technologies

💼 Practical Learning:

Hands-on RAG Projects & Enterprise AI Case Studies

💰 Program Fee:

₹17,500 + 18% GST

💰 Total Fee:

₹20,650

💳 EMI:

Starting From ₹4,032/Month*

🚀 Career Support:

Dedicated Career & Placement Assistance

🏢 Hiring Network:

850+ Companies in Our Hiring Network

🎯 Career Support Includes:

Resume Building & LinkedIn Optimization, Mock Interviews & Interview Preparation, Career Guidance & Job Opportunity Assistance

RAG Engineering Course in Chhatrapati Sambhajinagar (Aurangabad) — Quick Facts

🎓 Course:

RAG Engineering

🏢 Provider:

Uncodemy Edutech Pvt. Ltd.

📚 Total Learning:

140+ Hours of Learning

📖 Curriculum:

13 Modules

💼 Projects:

Hands-on RAG Projects + Enterprise RAG Capstone

🛠️ Tools:

Python, OpenAI APIs, LangChain, LlamaIndex, Vector Databases

🌐 Modes:

Live Online (offline only at Delhi & Noida centres; Chhatrapati Sambhajinagar (Aurangabad) learners join live online)

✅ Prior Coding:

Programming fundamentals helpful; Python taught from Module 2 onward. Basic LLM concepts built up in Module 3.

📜 Certificate:

Uncodemy RAG Engineering Program Certificate (verifiable online)

🗓️ Last Reviewed:

25 September 2026

List of Placed Students Trained by Uncodemy

Uncodemy’s RAG Engineering learners gain hands-on experience with document processing, embeddings, vector database technologies, retrieval pipelines and hybrid search methods. Following their technical training and career preparation, eligible learners may apply for opportunities such as RAG Engineer, AI Application Engineer and GenAI Engineer. Placement records for Chhatrapati Sambhajinagar (Aurangabad) learners are updated regularly — view verified outcomes at uncodemy.com/placement.

What is RAG Engineering?

RAG Engineering, or Retrieval-Augmented Generation, is the craft of joining large language models with external knowledge retrieval so that answers are anchored in genuine, current enterprise data rather than depending solely on the model's internal training. It spans document ingestion, chunking, embeddings, vector databases and retrieval pipelines that feed relevant context into an LLM when a query arrives — reducing hallucination and enabling AI systems to handle questions about private, domain-specific or rapidly changing information.

What does a RAG engineer do? A RAG engineer looks after document ingestion and chunking, generates embeddings and administers vector databases, designs retrieval pipelines, implements hybrid search, evaluates RAG accuracy, and rolls out production RAG systems. Daily responsibilities involve Python scripting, working with frameworks like LangChain and LlamaIndex, fine-tuning chunking and retrieval approaches, tracking relevance and faithfulness, and exposing RAG-powered APIs that applications can consume.

RAG Engineering vs LLM Engineering vs Generative AI

The main objective of RAG Engineering is to develop AI solutions that retrieve and ground information from enterprise knowledge. LLM Engineering explores LLM-powered applications across a wider scope, whereas Generative AI focuses strongly on 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.

Course Overview

The Uncodemy RAG Engineering course provides 5–6 months of live online instruction through 90+ sessions and 140+ hours of learning. Across 13 modules, learners progress from RAG and enterprise knowledge AI basics to Python-based RAG engineering, LLM fundamentals, API integration, document processing, data ingestion, text chunking, document transformation, embeddings, semantic search, vector databases, indexing, RAG workflows, advanced retrieval and reranking, hybrid search, knowledge retrieval, RAG evaluation, optimization, production APIs and deployment. The curriculum concludes with a 20-hour enterprise RAG capstone. Hands-on exercises using real tools form part of every module, while practical RAG projects and the enterprise capstone provide project-based experience. Training is conducted fully live online for learners in Chhatrapati Sambhajinagar and nationwide.

Who should join this course:

Developers wanting to specialise in retrieval-grounded AI systems and enterprise knowledge assistants.
Data scientists and ML engineers upskilling into RAG engineering and vector search.
Aspiring AI engineers looking to build production-ready RAG applications.
Working professionals moving into GenAI/RAG roles from software or analytics backgrounds.
Chhatrapati Sambhajinagar (Aurangabad)-based learners who want instructor-led live online training with hands-on projects.

Eligibility: Students joining the RAG Engineering course from Chhatrapati Sambhajinagar do not need mandatory programming experience. While having programming fundamentals can make the learning process easier, Python is taught beginning in Module 2. Basic LLM knowledge is helpful but is developed through Module 3 as part of the curriculum. For practical learning sessions, learners need a laptop and a stable internet connection.

Upcoming RAG Engineering Course Batches for Chhatrapati Sambhajinagar (Aurangabad) Learners

Students and working professionals from Chhatrapati Sambhajinagar can select a live online batch according to their preferred schedule. Classes are conducted in IST timings, with weekday as well as weekend options. Experience the learning approach by attending a free demo class, or enroll in an upcoming batch for flexible, practical training.

Weekdays

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

Weekends

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.

Enroll Now

Curriculum for RAG Engineering Training Course in Chhatrapati Sambhajinagar (Aurangabad)

RAG Engineering Course Syllabus in Chhatrapati Sambhajinagar (Aurangabad)

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 1: RAG & Enterprise Knowledge AI Foundations — 8 Hours

Introduction to RAG and enterprise knowledge AI
How RAG differs from fine-tuning and prompt-only approaches
Enterprise knowledge sources: documents, databases, APIs
RAG architecture overview: ingestion, retrieval, generation
Use cases: knowledge assistants, enterprise search, AI copilots
Challenges: accuracy, freshness, privacy and scale

Module 2: Python for RAG Engineering — 10 Hours

Python syntax, data structures and control flow
Functions, modules and packages
Working with APIs, JSON and environment variables
Virtual environments and dependency management
File I/O and handling document formats
Error handling and debugging for RAG applications

Module 3: LLM Fundamentals & API Integration — 10 Hours

LLM basics: tokens, context windows, temperature
Overview of LLM APIs (OpenAI/Gemini/Claude-style)
Authentication, rate limits and error handling
Streaming responses and async calls
Prompt basics for RAG-grounded answers
Building reusable API wrappers in Python

Module 4: Document Processing & Data Ingestion — 8 Hours

Document formats: PDF, HTML, DOCX, Markdown, CSV
Text extraction and cleaning pipelines
Metadata extraction and enrichment
Handling tables, images and structured content
Incremental ingestion and update strategies
Hands-on: building a document ingestion pipeline

Module 5: Text Chunking & Document Transformation — 8 Hours

Chunking strategies: fixed-size, recursive, semantic
Overlap, chunk size and their impact on retrieval
Document transformation and normalization
Metadata tagging for filtered retrieval
Handling multi-document and hierarchical content
Hands-on: comparing chunking strategies

Module 6: Embeddings & Semantic Search — 10 Hours

Text embeddings and semantic similarity
Embedding models: OpenAI, Hugging Face, open-source
Similarity metrics: cosine, dot product, Euclidean
Building and querying a semantic search index
Evaluating embedding quality for retrieval
Hands-on: building a semantic search pipeline

Module 7: Vector Databases & Indexing — 10 Hours

Vector databases: FAISS, ChromaDB, Pinecone, pgvector
Index types: flat, IVF, HNSW and trade-offs
Upserting, updating and deleting vectors
Metadata filtering and namespaces
Performance, scaling and cost considerations
Hands-on: building a vector search index

Module 8: Retrieval-Augmented Generation (RAG) — 12 Hours

RAG architecture: retriever, context builder, generator
Building end-to-end RAG pipelines with LangChain
Building RAG pipelines with LlamaIndex
Prompt design for grounded answers
Handling citations and source attribution
Hands-on: building a production-style RAG application

Module 9: Advanced Retrieval & Reranking — 10 Hours

Multi-query and query rewriting strategies
Reranking with cross-encoders and LLMs
Context compression and window expansion
Handling multi-hop and comparative queries
Retrieval fallback and error handling
Hands-on: adding reranking to a RAG pipeline

Module 10: Hybrid Search & Knowledge Retrieval — 10 Hours

Combining keyword (BM25) and vector search
Reciprocal rank fusion and score normalization
Elasticsearch/OpenSearch for hybrid retrieval
Metadata filters and structured queries
Knowledge graph integration basics
Hands-on: building a hybrid search system

Module 11: RAG Evaluation, Accuracy & Optimization — 12 Hours

Retrieval metrics: recall, precision, MRR, nDCG
Generation metrics: faithfulness, answer relevance
Building evaluation datasets for RAG
Automated evaluation with LLM-as-judge
Diagnosing and fixing retrieval failures
Hands-on: evaluating a RAG pipeline end-to-end

Module 12: Production RAG, APIs & Deployment — 12 Hours

Exposing RAG pipelines as APIs with FastAPI
Latency, caching and throughput optimization
Cost management for embeddings and LLM calls
Containerization and deployment with Docker
Monitoring, logging and observability for RAG
Hands-on: deploying a RAG API to the cloud

Module 13: Enterprise RAG Projects & Capstone — 20 Hours

End-to-end RAG application design and architecture
Ingestion + embeddings + vector DB + retrieval integration
Evaluation and optimization of the capstone application
Code review and portfolio preparation
Presentation and documentation of capstone project
Interview preparation around capstone work

#	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

Contacted by

Mr. Irshad

Curriculum & Technical Reviewer

[8+ Years Of Experience]

Specialization: RAG Engineering, Generative AI, Retrieval Systems

LinkedIn Profile

Get Your RAG Engineering Certification In Chhatrapati Sambhajinagar (Aurangabad)

Once you complete all 13 modules, the assignments and the Enterprise RAG capstone project, Uncodemy presents you with the RAG Engineering Program Certificate. This certificate documents the program name, duration, tools covered and completion date. It is verifiable online at certificate.uncodemy.com and acts as a record of your successful completion of the Uncodemy RAG Engineering program.

The certificate is linked to the practical work carried out 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.

Key Benefits of Our RAG Engineering Certification:

Program Completion :Provides a record of successful completion of the RAG Engineering program.
Online Verification :Certificate is verifiable online at certificate.uncodemy.com.
Practical Learning :Training includes hands-on RAG pipelines, retrieval evaluation and an enterprise capstone.

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

IIBA Endorsed Education Provider certificate — Uncodemy

Uncodemy RAG Engineering Program Certificate

Tools and Technologies Covered

OpenAI APIs
LangChain
LlamaIndex
Vector Databases
Hugging Face
FAISS
ChromaDB
Pinecone
FastAPI
Docker
<Elasticsearch
Git & GitHub

Apply Now

Introducing Uncodemy

Uncodemy is a career-oriented IT training institute operated by Uncodemy Edutech Pvt. Ltd. With 14+ years of training excellence, physical classroom centres in Delhi and Noida, and live online classes reaching learners all over India, Uncodemy conducts instructor-led training for 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.

Other courses for Chhatrapati Sambhajinagar (Aurangabad) learners

If you are still deciding on a direction, Uncodemy also runs LLM Engineering, AI Testing and Data Science courses for Chhatrapati Sambhajinagar (Aurangabad) 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.

Review

4.7/5500+ Google Reviews

5/530+ Sulekha Reviews

4.7/5500+ Just Dial Reviews

5/5Facebook Reviews

4/5130+ Mouthshut

5/554+ Trustindex Reviews

Instructors

The people who actually teach this course

Uncodemy maintains two roles as distinct that many institutes merge into one. Trainers lead the live sessions. Domain reviewers — separate specialists who don't teach the batch — check that the curriculum is technically accurate and current. For this program, curriculum and technical content are reviewed by Mr. Irshad, with additional review by Mr. Upendra Kumar Tiwari. That means the person teaching a course is not the only one checking whether its content is correct.

Every trainer and domain reviewer associated with Uncodemy is published by name, with their domain and background — you can look them up before you enrol rather than after.

Enroll in Our RAG Engineering Training for Chhatrapati Sambhajinagar (Aurangabad) Learners Today!

Start your RAG Engineering journey with Uncodemy’s course designed for learners in Chhatrapati Sambhajinagar (Aurangabad). Build practical expertise in document processing, embeddings, vector databases, retrieval pipelines, and hybrid search through live instructor-led sessions, hands-on projects, and an enterprise RAG capstone. Gain structured technical training and career preparation to strengthen your foundation in RAG engineering.

RAG Engineer Jobs and Career Scope in Chhatrapati Sambhajinagar (Aurangabad)+

Chhatrapati Sambhajinagar (Aurangabad)'s tech landscape — covering software services, captive units, fintech, SaaS and automotive R&D — is progressively creating in-house knowledge assistants, enterprise search solutions and AI copilots. Firms in Hinjewadi, Magarpatta, Kharadi, Baner and Viman Nagar are embedding RAG-driven capabilities into support platforms, document pipelines and internal tools, generating need for engineers who grasp document ingestion, chunking, embeddings, vector databases, retrieval workflows and hybrid search. BFSI, manufacturing-tech, healthcare, edtech and e-commerce verticals in Chhatrapati Sambhajinagar (Aurangabad) are likewise embracing RAG, and they require engineers who can anchor LLM responses in actual enterprise data instead of depending on model training alone. Interviews for RAG positions tend to emphasise retrieval pipeline architecture, chunking-strategy trade-offs and evaluation methodology most strongly. Applicants with software engineering or data/ML backgrounds alongside RAG engineering abilities are ideally placed for these positions.

Job Roles After This Course+

RAG Engineer, AI Application Engineer, GenAI Engineer, LLM/RAG Engineer, AI Solutions Engineer (Junior) and Search/Retrieval Engineer are the positions this course corresponds to. Every position entails creating retrieval-grounded AI systems, yet they vary in the particular RAG components and tools employed.

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

Most graduates begin as a RAG Engineer, AI Application Engineer or GenAI Engineer and advance into the others with two to four years of experience, as they develop depth in a particular RAG component or domain. Advancement generally entails owning bigger retrieval systems, leading projects, and specialising in evaluation, hybrid search or production deployment.

RAG Engineer Salary in Chhatrapati Sambhajinagar (Aurangabad) — Role-wise+

Salaries in Chhatrapati Sambhajinagar (Aurangabad) differ by experience level, sector and how robust your RAG engineering abilities are. The gap between the lowest and highest earners at the same title is frequently wider than freshers anticipate.

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

Sources reviewed: AmbitionBox, Glassdoor, Indeed and Uncodemy placement records. Reviewed: September 2026.

Salary ranges are indicative market ranges and should not be presented as guaranteed placement outcomes. Actual offers vary by role, skills, experience, employer and market conditions. Uncodemy does not guarantee any specific salary or employment outcome.

Top Sectors Hiring RAG Engineers in Chhatrapati Sambhajinagar (Aurangabad)+

Uncodemy collaborates with 850+ companies in our hiring network across India, covering large enterprises, mid-size firms and fast-growing startups, which provides learners exposure to more than one kind of hiring process and interview style. In Chhatrapati Sambhajinagar (Aurangabad) specifically, RAG engineering demand is rising in:

IT services and global capability centres — Hinjewadi, Kharadi and Magarpatta, creating internal knowledge assistants and enterprise search
Product companies and SaaS startups — creating AI copilots, RAG-based features and document Q&A systems
BFSI and fintech — implementing RAG for policy retrieval, compliance and customer support
Automotive and manufacturing-tech — RAG for technical documentation, diagnostics and knowledge bases
Healthcare, edtech and e-commerce — RAG for content retrieval, personalization and customer service

Every one of these sectors tends to weight skills somewhat differently, so as you get nearer to placement, it helps to tailor your capstone project toward the one or two sectors you're most interested in.

How to Become a RAG Engineer — Step by Step+

Becoming a RAG engineer is a step-by-step skill build, not a single course, and skipping steps usually shows up later as gaps in interviews or on the job.

Python fundamentals— syntax, data structures, APIs and environment management.
LLM fundamentals & API integration— tokens, context windows, API authentication and streaming.
Document processing & chunking— extraction, cleaning, chunking strategies and metadata.
Embeddings & semantic search— embedding models, similarity metrics and index building.
Vector databases— FAISS, ChromaDB, Pinecone, pgvector, indexing and filtering.
RAG pipeline & advanced retrieval— LangChain/LlamaIndex, reranking, hybrid search.
Evaluation, production deployment & capstone— RAGAS, LangSmith, FastAPI, Docker and the enterprise RAG project.

Typical timeline: 5–6 months of consistent study to reach interview-ready standard, assuming focused hours most days rather than occasional binge sessions.

How This Course Compares to Other AI Courses in Chhatrapati Sambhajinagar (Aurangabad)+

Chhatrapati Sambhajinagar (Aurangabad) has dozens of AI training options at very different price points, ranging from short weekend workshops to multi-month bootcamps. Here is an honest comparison, so you can judge the fit yourself rather than relying on marketing claims alone.

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

Compared to Uncodemy's LLM Engineering course (5–6 months, focused on building, fine-tuning and deploying LLM-powered applications broadly) and Generative AI & Agentic AI course (~3–4 months, focused on GenAI apps and autonomous agents), this RAG Engineering program is the most specialised in retrieval-grounded systems — it includes dedicated modules on document processing, chunking, embeddings, vector databases, advanced retrieval, hybrid search and evaluation. If your goal is a full RAG engineering career transition, this is the right fit. If you want broad LLM application development, LLM Engineering is a closer match. If you are specifically building AI agents, the Generative AI & Agentic AI course is a closer match.

Where we are not the cheapest option: shorter self-paced courses cost less. If your goal is a quick RAG refresher rather than a full career transition, a shorter program may suit you better, and it's worth being honest with yourself about which of the two you actually need.

Online vs Offline vs Hybrid — Which Should You Choose?+

Offline classroom is available at our Delhi and Noida centres; Chhatrapati Sambhajinagar (Aurangabad) learners typically join live online. Live online gives you the same instructor-led experience with zero commute, and all sessions are recorded for revision. Offline suits learners who prefer face-to-face interaction and are based near Delhi or Noida. Hybrid is not currently offered for this program. For Chhatrapati Sambhajinagar (Aurangabad)-based working professionals, live online is usually the most practical choice — you get real-time doubt clearing, hands-on labs and lifetime access to recordings without travelling.

Is this course right for you?+

If you want...	Recommend
Full RAG Engineering career transition	This program
Already know LLM Engineering fundamentals, want to specialise in retrieval systems	This program
Building AI agents specifically	Consider Generative AI & Agentic AI course
Broad LLM application development	Consider LLM Engineering course

Frequently Asked Questions

1. What is the duration of the RAG Engineering course in Chhatrapati Sambhajinagar (Aurangabad)?▼

The program runs for 5–6 months and includes 140+ hours of learning across 90+ live sessions. Weekday and weekend batches are both available.

2. Do I need prior Python or LLM experience?▼

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.

3. What tools are covered in this course?▼

15+ tools including Python, OpenAI APIs, Hugging Face, LangChain, LlamaIndex, LangGraph, FAISS, ChromaDB, Pinecone, PostgreSQL/pgvector, Elasticsearch/OpenSearch, FastAPI, Docker, Git/GitHub and cloud deployment tools.

4. Does the course cover vector databases and hybrid search?▼

Yes. Module 7 covers Vector Databases & Indexing (10 hours) and Module 10 covers Hybrid Search & Knowledge Retrieval (10 hours). Both are core components with hands-on exercises.

5. What is the capstone project?▼

The capstone is Module 13: Enterprise RAG Projects & Capstone (20 hours). You build an end-to-end RAG application applying document processing, embeddings, vector search, retrieval, evaluation and deployment.

6. Are the classes live or pre-recorded?▼

All 90+ sessions are live and instructor-led, with recordings available afterwards for revision.

7. What is the fee for the RAG Engineering course in Chhatrapati Sambhajinagar (Aurangabad)?▼

The program fee is ₹17,500 plus 18% GST. Total fee is ₹20,650. The same fee applies in all modes and cities.

8. Are EMI options available?▼

EMI starts from ₹4,032 per month. The exact amount, tenure and eligibility depend on the applicable payment or financing option — a counsellor can confirm your plan.

9. Is there a demo or trial class before I enrol?▼

Yes. You can attend a demo session to see the teaching style, meet the trainer and review the curriculum before deciding. Contact us to book a demo.

10. Do you provide placement assistance?▼

We provide Dedicated Career & Placement Assistance — resume building, LinkedIn optimisation, portfolio review, interview preparation, mock interviews and opportunity sharing through our network of 850+ companies. We do not guarantee employment.

11. Can I get an entry-level RAG Engineer role after this course?▼

Entry-level RAG Engineer roles in the market range around ₹5–9 LPA (indicative). Outcomes vary by skills, experience, employer and market conditions. Uncodemy does not guarantee any specific salary or employment outcome.

12. What is the salary of a RAG Engineer in Chhatrapati Sambhajinagar (Aurangabad)?▼

Indicative market ranges: Entry-Level ₹5–9 LPA, RAG/GenAI Engineer ₹8–15 LPA, Senior RAG Engineer ₹18–30 LPA, Lead AI/RAG Engineer ₹25–40 LPA. Actual offers vary by role, skills, experience, employer and market conditions. Uncodemy does not guarantee any specific salary.

13. What job roles can I apply for after this course?▼

RAG Engineer, AI Application Engineer, GenAI Engineer, LLM/RAG Engineer, AI Solutions Engineer (Junior) and Search/Retrieval Engineer.

14. Who is eligible for this RAG Engineering course?▼

Developers wanting to specialise in retrieval-grounded AI systems, data scientists/ML engineers upskilling into RAG engineering, aspiring AI engineers, and working professionals moving into GenAI/RAG roles.

15. Are the salary, placement and review figures on this page guaranteed?▼

Statistics are based on Uncodemy's internal records and publicly available review-platform information. Placement and salary outcomes vary by learner, skills, experience, location, employer and market conditions. Career and placement assistance does not constitute a guarantee of employment or a specific salary package.

16. Is classroom training available in Chhatrapati Sambhajinagar (Aurangabad)?▼

This course is delivered live online nationwide, including for Chhatrapati Sambhajinagar (Aurangabad) learners. Classroom training is available at our Delhi and Noida centres for other courses. Chhatrapati Sambhajinagar (Aurangabad) learners typically join live online.

17. Is Uncodemy's RAG Engineering certificate affiliated with OpenAI or Pinecone?▼

No. 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.

Why Trust This Page? Discover Why Students Choose Uncodemy

How we maintain this page

Content written by:Mr. Rahul
Content rechecked and verified by: Mr. Irshad
Course curriculum verified by:Mr. Irshad
Technical content verified by: Mr. Irshad
Fee verified by: Admin Department
Placement figures sourced from internal placement records
Review counts verified against respective third-party platforms
Last reviewed: 25 Sep 2026

Written by:Mr. Rahul

Reviewed by: Mr. Upendra Kumar Tiwari

Final editorial check:Mr. Gangesh Gaunr, Director at Uncodemy

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The RAG Engineering course is delivered fully live online. Chhatrapati Sambhajinagar (Aurangabad) learners typically join live online, and our nearest classroom training centres for other courses are in Delhi and Noida. Uncodemy's Delhi centre is located on the 2nd & 3rd Floor, Maa Katyayni Complex, MB-1E, Madhuban Road, Shakarpur Extension — a 10–15 minute walk from Nirman Vihar Metro Station and Laxmi Nagar Metro Station. Uncodemy's Noida centre is at B 14-15, Udhyog Marg, Block B, Sector 1, Noida.

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2nd & 3rd Floor, Maa Katyayni Complex, MB-1E, Madhuban Rd, next to DSEU Ambedkar College, Laxmi Nagar, Shakarpur Extension, Shakarpur, Delhi, 110092

Noida Centre

B, 14-15, Udhyog Marg, Block B, Sector 1, Noida, Uttar Pradesh 201301

Phone+91 9818366550 / +91 8766313646

Emailinfo@uncodemy.com

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RAG Engineering Training Course in Chhatrapati Sambhajinagar (Aurangabad) by Uncodemy

Special Offer: RAG Engineering Program

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RAG Engineering Course in Chhatrapati Sambhajinagar (Aurangabad) — Quick Facts

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RAG Engineering vs LLM Engineering vs Generative AI

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Curriculum for RAG Engineering Training Course in Chhatrapati Sambhajinagar (Aurangabad)

RAG Engineering Course Syllabus in Chhatrapati Sambhajinagar (Aurangabad)

Module 1: RAG & Enterprise Knowledge AI Foundations — 8 Hours

Module 2: Python for RAG Engineering — 10 Hours

Module 3: LLM Fundamentals & API Integration — 10 Hours

Module 4: Document Processing & Data Ingestion — 8 Hours

Module 5: Text Chunking & Document Transformation — 8 Hours

Module 6: Embeddings & Semantic Search — 10 Hours

Module 7: Vector Databases & Indexing — 10 Hours

Module 8: Retrieval-Augmented Generation (RAG) — 12 Hours

Module 9: Advanced Retrieval & Reranking — 10 Hours

Module 10: Hybrid Search & Knowledge Retrieval — 10 Hours

Module 11: RAG Evaluation, Accuracy & Optimization — 12 Hours

Module 12: Production RAG, APIs & Deployment — 12 Hours

Module 13: Enterprise RAG Projects & Capstone — 20 Hours

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RAG Engineer Salary in Chhatrapati Sambhajinagar (Aurangabad) — Role-wise+

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How This Course Compares to Other AI Courses in Chhatrapati Sambhajinagar (Aurangabad)+

Online vs Offline vs Hybrid — Which Should You Choose?+

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Frequently Asked Questions

1. What is the duration of the RAG Engineering course in Chhatrapati Sambhajinagar (Aurangabad)?▼

2. Do I need prior Python or LLM experience?▼

3. What tools are covered in this course?▼

4. Does the course cover vector databases and hybrid search?▼

5. What is the capstone project?▼

6. Are the classes live or pre-recorded?▼

7. What is the fee for the RAG Engineering course in Chhatrapati Sambhajinagar (Aurangabad)?▼

8. Are EMI options available?▼

9. Is there a demo or trial class before I enrol?▼

10. Do you provide placement assistance?▼

11. Can I get an entry-level RAG Engineer role after this course?▼

12. What is the salary of a RAG Engineer in Chhatrapati Sambhajinagar (Aurangabad)?▼

13. What job roles can I apply for after this course?▼

14. Who is eligible for this RAG Engineering course?▼

15. Are the salary, placement and review figures on this page guaranteed?▼

16. Is classroom training available in Chhatrapati Sambhajinagar (Aurangabad)?▼

17. Is Uncodemy's RAG Engineering certificate affiliated with OpenAI or Pinecone?▼

Why Trust This Page? Discover Why Students Choose Uncodemy

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Nearest Classroom Training Centres

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Uncodemy?