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🔥 Most Popular in Thiruvananthapuram

RAG Engineering Training Course in Thiruvananthapuram by Uncodemy

Q: What is the duration of the RAG Engineering course in Thiruvananthapuram?

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 Thiruvananthapuram?

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.

Uncodemy brings its 5–6 month instructor-led live online RAG Engineering program to learners in Thiruvananthapuram. The syllabus covers document ingestion & chunking, embeddings, vector databases, retrieval pipelines, hybrid search and production RAG deployment. Designed for developers, data scientists/ML engineers and aspiring AI engineers, it spans 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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Learn from 8+ years experienced professionals

Hands-on Automation Projects

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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

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EMI Options Available

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Program Details

RAG Engineering Course — Live Online Program Practical, hands-on learning aimed at building real RAG engineering skills and interview readiness.

A structured 5–6 month instructor-led live online program that covers embeddings, vector databases, retrieval pipelines, hybrid search, and production RAG deployment, with projects, mentoring and career support built in.

📚 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 Thiruvananthapuram — 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; Thiruvananthapuram 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 learners who complete the RAG Engineering training program build practical skills in document processing, embeddings, vector databases, retrieval pipelines and hybrid search. Once training and career preparation are finished, eligible learners may apply for roles like RAG Engineer, AI Application Engineer and GenAI Engineer. Placement data for Thiruvananthapuram learners is refreshed at regular intervals — view verified outcomes at uncodemy.com/placement.

What is RAG Engineering?

RAG Engineering — short for Retrieval-Augmented Generation — is the discipline of connecting large language models to an external knowledge retrieval layer, so that AI responses are grounded in real, current enterprise data instead of leaning solely on what the model was trained on. The work involves ingesting documents, chunking them, generating embeddings, storing them in vector databases and designing retrieval pipelines that surface the right context to the LLM at query time. This reduces hallucination and lets AI systems answer questions about private, domain-specific or rapidly changing information.

What does a RAG engineer do? On a typical day, a RAG engineer oversees document ingestion and chunking, builds embeddings and manages vector databases, designs retrieval pipelines, implements hybrid search, evaluates RAG accuracy, and deploys production RAG systems. The role involves Python scripting, working with frameworks such as LangChain and LlamaIndex, tuning chunking and retrieval strategies, measuring relevance and faithfulness, and exposing RAG-powered APIs that applications can consume.

RAG Engineering vs LLM Engineering vs Generative AI

RAG Engineering is centred on creating retrieval-grounded AI systems built on enterprise knowledge, while LLM Engineering covers LLM-powered applications more broadly and Generative AI focuses 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

Spread across 5–6 months, this live online RAG Engineering program is delivered through 90+ instructor-led sessions and 140+ hours of structured learning. Its 13 modules build in a deliberate sequence — beginning with RAG & enterprise knowledge AI foundations, then moving through Python for RAG engineering, LLM fundamentals & API integration, document processing & data ingestion, text chunking & document transformation, embeddings & semantic search, vector databases & indexing, RAG itself, advanced retrieval & reranking, hybrid search & knowledge retrieval, RAG evaluation & optimization, and production RAG APIs & deployment, before closing with a 20-hour enterprise RAG capstone. Hands-on exercises with real tools are embedded in every module. Learners based in Thiruvananthapuram attend the same live online sessions as participants nationwide.

Who should join this course:

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

Eligibility: Programming fundamentals help but are not mandatory — Python is taught starting from Module 2. Some familiarity with basic LLM concepts is useful but is built up in Module 3. A laptop and stable internet connection are required for the practical sessions.

Upcoming RAG Engineering Course Batches for Thiruvananthapuram Learners

Select a batch that fits your timetable. Live online batches are scheduled in IST. Book a free demo class to get a feel for the teaching style, or join one of our upcoming weekday and weekend live batches — structured for working professionals and students alike, 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 Thiruvananthapuram

RAG Engineering Course Syllabus in Thiruvananthapuram

The syllabus is divided into 13 modules covering 140 hours in total. Its order reflects how RAG engineering works in the field: foundations → Python → LLM fundamentals & API integration → document processing & chunking → embeddings & semantic search → vector databases → RAG → advanced retrieval/reranking → hybrid search → evaluation → production deployment → capstone. Real tools are used throughout the practical exercises, and the program concludes 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 Thiruvananthapuram

Once all 13 modules, the assignments and the Enterprise RAG capstone project are completed, learners receive the Uncodemy RAG Engineering Program Certificate. This certificate records the program name, duration, tools covered and completion date. It can be verified online at certificate.uncodemy.com and serves as documentation of successful completion of the Uncodemy RAG Engineering program.

The certificate reflects the practical work done during the program — 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-focused IT training institute operated by Uncodemy Edutech Pvt. Ltd. Backed by 14+ years of training excellence, physical classroom centres in Delhi and Noida, and live online classes reaching learners across India, Uncodemy runs instructor-led training for emerging technologies.

Through this RAG Engineering program, learners build retrieval pipelines, hybrid search systems, production RAG APIs and complete an enterprise RAG capstone project. The training reflects how retrieval-grounded AI systems are engineered in production — covering document processing, embeddings, vector search, retrieval evaluation and deployment. You can review the full module-by-module curriculum before enrolling.

In addition to training, Uncodemy offers instructor-led sessions, practical assignments, project-based learning, interview preparation and career support. The scope of that support — and what it does not include — is documented in our career support policy, together with the eligibility conditions published before enrolment rather than after payment.

Other courses for Thiruvananthapuram learners

If you're still weighing your options, Uncodemy also runs LLM Engineering, AI Testing and Data Science courses for Thiruvananthapuram and Kerala 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 separates two roles that most institutes tend to merge. Trainers run the live sessions. Domain reviewers — independent specialists who do not teach the batch — verify that the curriculum is technically accurate and up to date. 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 person verifying whether its content is correct.

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

Enroll in Our RAG Engineering Training for Thiruvananthapuram Learners Today!

Sign up for Uncodemy's RAG Engineering course designed for Thiruvananthapuram learners today! Build practical skills in document processing, embeddings, vector databases, retrieval pipelines and hybrid search through instructor-led live online training, hands-on projects and an enterprise RAG capstone. Begin your RAG engineering journey through structured training and career preparation.

RAG Engineer Jobs and Career Scope in Thiruvananthapuram+

Thiruvananthapuram's technology ecosystem — anchored by Technopark, Infopark and the state's growing startup base, alongside government and PSU tech, aerospace and space-tech, healthcare, biotech, education, tourism-tech and fintech — is steadily adopting internal knowledge assistants, enterprise search platforms and AI copilots. Companies around Kazhakkoottam, Kowdiar, Vazhuthacaud, Palayam, Sasthamangalam and the Technopark belt are integrating RAG-based features into customer support, document workflows and internal tooling, creating demand for engineers who understand document ingestion, chunking, embeddings, vector databases, retrieval pipelines and hybrid search. Space-tech and aerospace-adjacent organisations in and around the capital also use RAG for technical documentation, diagnostics and knowledge bases, and they need engineers who can anchor LLM answers in real enterprise data rather than relying on model training alone. Interviews for RAG roles in the region 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 begin as a RAG Engineer, AI Application Engineer or GenAI Engineer and shift into the other roles with two to four years of experience, as they build depth in a specific RAG component or domain. Progression usually involves owning larger retrieval systems, leading projects, and specialising in evaluation, hybrid search or production deployment.

RAG Engineer Salary in Thiruvananthapuram — Role-wise+

Salaries in Thiruvananthapuram shift with 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 Thiruvananthapuram+

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 Thiruvananthapuram specifically, RAG engineering demand is emerging in:

IT services and Technopark-based firms — Kazhakkoottam and the Technopark belt, building internal knowledge assistants and enterprise search
Product companies and SaaS startups — Infopark and the capital's startup ecosystem, building AI copilots, RAG features and document Q&A systems
Government, PSU and public-sector tech — deploying RAG for policy retrieval, citizen-facing services and document-heavy workflows
Aerospace, space-tech and engineering R&D — RAG for technical documentation, diagnostics and knowledge bases
Healthcare, biotech, education and tourism-tech — 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 Thiruvananthapuram+

Thiruvananthapuram 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; Thiruvananthapuram 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 Thiruvananthapuram-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 Thiruvananthapuram?▼

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 Thiruvananthapuram?▼

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 Thiruvananthapuram?▼

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 Thiruvananthapuram?▼

This course is delivered live online nationwide, including for Thiruvananthapuram learners. Classroom training is available at our Delhi and Noida centres for other courses. Thiruvananthapuram 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. Thiruvananthapuram 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

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

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RAG Engineering Training Course in Thiruvananthapuram by Uncodemy

Special Offer: RAG Engineering Program

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Curriculum for RAG Engineering Training Course in Thiruvananthapuram

RAG Engineering Course Syllabus in Thiruvananthapuram

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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How to Become a RAG Engineer — Step by Step+

How This Course Compares to Other AI Courses in Thiruvananthapuram+

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 Thiruvananthapuram?▼

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 Thiruvananthapuram?▼

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 Thiruvananthapuram?▼

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 Thiruvananthapuram?▼

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?