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🔥 Most Popular in Delhi

RAG Engineering Training Course in Delhi by Uncodemy

Uncodemy delivers a focused 5–6 month instructor-led RAG Engineering program for learners in Delhi. The curriculum covers embeddings, vector databases, retrieval pipelines, hybrid search and production RAG deployment. Classroom sessions are available at the Delhi centre, with live online options also offered. Program fee is ₹17,500 + 18% GST, total ₹20,650.

Google4.7/5 (500+)Sulekha5/5 (30+)Just Dial4.7/5 (500+)Facebook5/5Mouthshut4/5 (130+)Trustindex5/5 (54+)

1,200+ Reviews Across Major Platforms*

*Ratings and review counts reflect current publicly available platform information and may change over time.

⚡SPECIAL LIMITED TIME OFFER

Special Offer: RAG Engineering Program

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Total ₹20,650
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Expert Trainers

30+ Industry Trainers & Mentors

Hands-on RAG Projects & Enterprise Case Studies

Build real retrieval-grounded AI systems, not just theory

Lifetime Access

Get lifetime access to recorded sessions & materials

Dedicated Career & Placement Assistance

Resume Building, Mock Interviews & Job Support

Certified Course

Certificate of Completion from Uncodemy

Flexible Batches

Weekday & Weekend Online & Offline

Why Choose
Uncodemy?

14+
Years of Training Excellence
850+
Companies in Our Hiring Network
54,000+
Learners Trained
200+
Corporate Associations
100%
Placement Support
Online & Offline Classes
Weekend & Weekday Batches
100% Practical Training
Affordable Fees
EMI Options Available
Dedicated Support

Program Details

RAG Engineering Course — Live Online Program Practical, hands-on training designed to build RAG engineering skills and prepare learners for technical interviews.
A comprehensive 5–6 month instructor-led live online program designed to develop practical skills in embeddings, vector databases, retrieval pipelines, hybrid search, and production RAG deployment, along with real-world 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 Delhi — 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 centres; Delhi 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:
3 October 2026

List of Placed Students Trained by Uncodemy

Learners completing the RAG Engineering program gain practical skills in document processing, embeddings, vector databases, retrieval pipelines and hybrid search. These skills prepare them for roles such as RAG Engineer, AI Application Engineer and GenAI Engineer. Placement records for Delhi learners are updated regularly — view verified outcomes at uncodemy.com/placement.

What is RAG Engineering?

RAG Engineering (Retrieval-Augmented Generation) combines large language models with external knowledge retrieval so that responses remain grounded in real, up-to-date enterprise data instead of relying solely on the model's training cut-off. The discipline covers document ingestion, chunking, embeddings, vector databases and retrieval pipelines that supply relevant context to an LLM at query time, reducing hallucination and enabling answers based on private or frequently changing information.

What does a RAG engineer do? A RAG engineer works on document ingestion and chunking, builds embeddings, manages vector databases, designs retrieval pipelines, implements hybrid search, evaluates RAG accuracy and deploys production systems. Day-to-day tasks typically involve Python, frameworks such as LangChain and LlamaIndex, tuning chunking and retrieval strategies, measuring relevance and faithfulness, and exposing RAG-powered APIs.

RAG Engineering vs LLM Engineering vs Generative AI & Agentic AI

Where RAG Engineering is concerned with building retrieval-grounded AI systems on enterprise knowledge, LLM Engineering encompasses LLM-powered applications broadly and Generative AI & Agentic AI centres on autonomous agents.

Aspect RAG Engineering LLM Engineering Generative AI & Agentic 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) Varies by role
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 RAG Engineering program spans 5–6 months with 90+ live sessions and 140+ hours of learning across 13 modules. The learning path moves from foundations through Python, LLM fundamentals and API integration, document processing and chunking, embeddings and semantic search, vector databases, core RAG, advanced retrieval and reranking, hybrid search, evaluation and optimisation, production deployment, and ends with a 20-hour enterprise RAG capstone. Delhi learners can attend classroom sessions at the Delhi centre or join live online batches.

Who should join this course:

  • Developers who want 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 aiming to build production-ready RAG applications.
  • Working professionals transitioning into GenAI or RAG-focused roles.
  • Delhi-based learners who prefer the option of in-person classroom training.

Eligibility: Programming fundamentals are helpful. Python is taught from the basics in Module 2. Familiarity with basic LLM concepts is an advantage but is covered in Module 3. A laptop and stable internet connection are required for live sessions and project work.

Upcoming RAG Engineering Course Batches for Delhi Learners

Choose a batch timing that best fits your schedule. Live online batches, IST timings. Reserve a free demo class first-hand, or step into our upcoming weekend and weekday live batches — suited to working professionals as well as students, with flexible timings and hands-on 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 Delhi

RAG Engineering Course Syllabus in Delhi

The 13-module curriculum delivers 140 hours of structured learning. It follows the natural flow of RAG engineering: foundations → Python → LLM fundamentals & API integration → document processing & chunking → embeddings & semantic search → vector databases → RAG → advanced retrieval/reranking → hybrid search → evaluation → production deployment → 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

IR

Mr. Irshad

Curriculum & Technical Reviewer

[8+ Years Of Experience]

Specialization: RAG Engineering, Generative AI, Retrieval Systems

LinkedInProfile

Get Your RAG Engineering Certification In Delhi

On successful completion of all 13 modules, assignments and the Enterprise RAG capstone project, learners receive the Uncodemy RAG Engineering Program Certificate. The certificate records the program name, duration, tools covered and completion date, and carries a unique verification reference.

The certificate is linked to the practical work completed during the program, including hands-on RAG pipeline construction, retrieval evaluation and the 20-hour Enterprise RAG capstone project.

Key Benefits of Our RAG Engineering Certification:

  • Program Completion :Record of successful program completion.
  • Online Verification :Online verification of certificate authenticity.
  • Practical Learning :Evidence of practical RAG pipeline and evaluation work.

Certificates can be verified at certificate.uncodemy.com. This is an independent Uncodemy certificate and is not affiliated with, endorsed by, or a credential of OpenAI, Pinecone or any other vendor whose tools are taught in the course.

Uncodemy Certificate Verification Online Certificate Verification & Download certificate.uncodemy.com
IIBA Endorsed Education Provider certificate — Uncodemy
Uncodemy RAG Engineering Program Certificate
Uncodemy MSME registration certificate

Tools and Technologies Covered

Apply Now

Introducing Uncodemy

Uncodemy is a career-focused IT training institute operated by Uncodemy Edutech Pvt. Ltd. With more than 14 years of training excellence, the institute maintains classroom centres in Delhi and Noida and offers live online classes for learners across India.

In the RAG Engineering program, participants build retrieval pipelines, hybrid search systems, production RAG APIs and complete an enterprise-level RAG capstone project covering document processing, embeddings, vector search, retrieval evaluation and deployment.

Training is delivered through instructor-led sessions, practical assignments, project-based learning, interview preparation and dedicated career support. Career support policy and eligibility conditions are published before enrolment. You can browse the full module-by-module curriculum before you enrol.

Other courses for Delhi learners

Other courses for Delhi learners include LLM Engineering, Generative AI & Agentic AI, AI Testing and Data Science.

Portfolio and capstone project review:Your hands-on RAG pipelines, retrieval evaluation work and enterprise RAG capstone are reviewed and refined into a portfolio an interviewer will find credible.

Resume building and LinkedIn optimisation: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 improve.

Opportunity sharing:Relevant openings from our network of 850+ companies are shared with you, with support through the application and interview process.

Continued support:Dedicated career and placement assistance is provided. Uncodemy does not guarantee employment or any specific salary outcome.

Review
Google Reviews
4.7/5500+ Google Reviews
Sulekha Reviews
5/530+ Sulekha Reviews
Just Dial Reviews
4.7/5500+ Just Dial Reviews
Facebook Reviews
5/5Facebook Reviews
Mouthshut Reviews
4/5130+ Mouthshut
Trustindex Reviews
5/554+ Trustindex Reviews

Instructors

The people who actually teach this course

Live sessions are conducted by the Uncodemy trainer team. The institute works with 30+ industry trainers and mentors who collectively bring more than 10 years of average corporate training experience.

Uncodemy maintains a clear separation between trainers who deliver the sessions and domain reviewers who verify curriculum accuracy. For the RAG Engineering program, curriculum and technical content are reviewed by Mr. Irshad, with additional review by Mr. Upendra Kumar Tiwari.

Every trainer and reviewer is published by name together with profile links where available.

Enroll in Our RAG Engineering Training for Delhi Learners Today!

Secure your place in an upcoming batch and begin building production-ready retrieval-grounded AI systems. Classroom training is available at the Delhi centre, with live online options for flexible schedules. Contact the admissions team to discuss batch timings, fee options and next steps.

RAG Engineer Jobs and Career Scope in Delhi+

Delhi's technology ecosystem — spanning software services, captive centres, fintech, SaaS and media — is increasingly building internal knowledge assistants, enterprise search tools and AI copilots. Companies across Connaught Place, Nehru Place, Okhla, Saket and the Gurugram border are integrating RAG-based features into support systems, document workflows and internal tooling, creating demand for engineers who understand document ingestion, chunking, embeddings, vector databases, retrieval pipelines and hybrid search. BFSI, media, healthcare, edtech and e-commerce sectors in Delhi are also adopting RAG, and they need engineers who can ground LLM answers in real enterprise data rather than relying on model training alone. Interviews for RAG roles tend to weight retrieval pipeline design, chunking-strategy trade-offs and evaluation methodology most heavily. Candidates with software engineering or data/ML experience plus RAG engineering skills are well-positioned for these roles.

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 roles this course maps to. Each role involves building retrieval-grounded AI systems, but they differ in the specific RAG components and tools used.
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 start as a RAG Engineer, AI Application Engineer or GenAI Engineer and move into the others with two to four years of experience, as they build depth in a specific RAG component or domain. Progression typically involves owning larger retrieval systems, leading projects, and specialising in evaluation, hybrid search or production deployment.

RAG Engineer Salary in Delhi — Role-wise+

Salaries in Delhi vary by experience level, sector and how strong your RAG engineering skills are. The gap between the lowest and highest earners at the same title is often wider than freshers expect.
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 Delhi+

Uncodemy works with 850+ companies in our hiring network across India, spanning large enterprises, mid-size firms and fast-growing startups, which gives learners exposure to more than one kind of hiring process and interview style. In Delhi specifically, RAG engineering demand is emerging in:
  • IT services and global capability centres — Connaught Place, Nehru Place and Okhla, building internal knowledge assistants and enterprise search
  • Product companies and SaaS startups — building AI copilots, RAG-based features and document Q&A systems
  • BFSI and fintech — deploying RAG for policy retrieval, compliance and customer support
  • Media and broadcast — RAG for content retrieval, archiving and knowledge bases
  • Healthcare, edtech and e-commerce — RAG for content retrieval, personalization and customer service
Each of these sectors tends to weight skills slightly differently, so as you get closer 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 Delhi+

Delhi 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
Duration5–6 months
Learning hours140+
Live sessions90+
Tools15+
Capstone20-hour Enterprise RAG project
Career supportDedicated 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; Delhi learners can join classroom sessions for this program. 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 Delhi-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 Delhi?▼

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 Delhi?▼

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 Delhi?▼

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 Delhi?▼

Yes. This course is delivered in classroom mode at our Delhi centre at 2nd & 3rd Floor, Maa Katyayni Complex, MB-1E, Madhuban Rd, next to DSEU Ambedkar College, Laxmi Nagar, Shakarpur Extension, Shakarpur, Delhi, 110092. Live online options are also available.

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:

Written by:Mr. Rahul

Reviewed by: Mr. Upendra Kumar Tiwari

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

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Nearest Classroom Training Centres

Uncodemy offers its RAG Engineering program to learners across Delhi through two flexible learning options: live online classes that can be attended from anywhere, and classroom training at Uncodemy’s new Delhi centre at 2nd & 3rd Floor, Maa Katyayni Complex, MB-1E, Madhuban Road, next to DSEU Ambedkar College, Laxmi Nagar, Shakarpur Extension, Shakarpur, Delhi 110092. The centre is conveniently located around a 10–15 minute walk from Nirman Vihar Metro Station. Delhi learners can also join the same RAG Engineering batches at the Noida centre or choose live online training, with the same experienced trainers, practical curriculum, and hands-on RAG projects across all learning formats.

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Delhi Centre
2nd & 3rd Floor, Maa Katyayni Complex, MB-1E, Madhuban Rd, next to DSEU Ambedkar College, Laxmi Nagar, Shakarpur Extension, Shakarpur, Delhi, 110092
Phone+91 9818366550 / +91 8766313646
Emailinfo@uncodemy.com
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