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🔥 Most Popular in Mangaluru

LLM Engineering Training Course in Mangaluru by Uncodemy

Learners in Mangaluru can enrol in Uncodemy's 5–6 month instructor-led live online LLM Engineering program. The curriculum spans LLM APIs, embeddings & vector databases, RAG, fine-tuning, LLM agents & tool calling, and deployment. Crafted for developers, data scientists/ML engineers and aspiring AI engineers, the program offers 13 modules, 100+ live sessions, 160+ hours and an enterprise LLM capstone. Fee: ₹17,500 + 18% GST, total ₹20,650. EMI starting from ₹4,032/month.

Google4.7/5 (500+)Sulekha5/5 (30+)Just Dial4.7/5 (500+)Facebook5/5Mouthshut4/5 (130+)Trustindex5/5 (54+)
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Special Offer: LLM Engineering Program

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Total ₹20,650
Incl. 18% GST
EMI AVAILABLE
Starting ₹4,032/M*
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Expert Trainers

Learn from 8+ years experienced professionals

Hands-on Automation Projects

Build real LLM test suites, 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

LLM Engineering Course — Live Online Program Hands-on learning focused on practical LLM engineering skills and interview preparation.
A well-structured 5–6 month instructor-led live online program covering LLM APIs, embeddings & vector databases, RAG, fine-tuning, LLM agents & tool calling, and deployment with guided projects, mentoring and career support.
📚 Learning:
160+ Hours of Learning
⏱️ Program Duration:
5–6 Months
🎓 Live Training:
100+ Live Sessions
🛠️ Technology Coverage:
15+ Tools & Technologies
💼 Practical Learning:
Hands-on Projects, RAG Applications & 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

LLM Engineering Course in Mangaluru — Quick Facts

🎓 Course:
LLM Engineering
🏢 Provider:
Uncodemy Edutech Pvt. Ltd.
📚 Total Learning:
160+ Hours of Learning
📖 Curriculum:
13 Modules
💼 Projects:
Hands-on Projects + Enterprise LLM Capstone
🛠️ Tools:
LLM APIs/SDKs, LangChain, Vector DBs, Python, Hugging Face, Prompt Engineering, Fine-Tuning, Evaluation, Git
🌐 Modes:
Live Online (offline only at Delhi & Noida centres; Mangaluru learners join live online)
✅ Prior Coding:
Programming fundamentals helpful; Python taught from Module 2 onward. Basic ML/NLP built up in Module 3.
📜 Certificate:
Uncodemy LLM Engineering Program Certificate (verifiable online)
🗓️ Last Reviewed:
25 September 2026

List of Placed Students Trained by Uncodemy

Uncodemy graduates who complete the LLM Engineering training program build practical expertise in LLM APIs, RAG pipelines, fine-tuning, vector databases and LLM agents. After finishing their training and career preparation, eligible candidates may pursue roles such as LLM Engineer, AI Engineer and RAG/AI Application Engineer. Placement outcomes for Mangaluru learners are refreshed routinely — explore verified results at uncodemy.com/placement.

What is LLM Engineering?

LLM engineering is the craft of architecting, constructing, fine-tuning and deploying applications that run on large language models. Where conventional software engineering leans on deterministic logic and data science centres on predictive modelling from data, LLM engineering operates with probabilistic models, prompt design, retrieval pipelines and model adaptation. It resides at the meeting point of software engineering, machine learning and product development, converting raw LLM capabilities into trustworthy, production-grade features.

What does an LLM engineer do? An LLM engineer works with LLM APIs and SDKs, builds RAG pipelines with embeddings and vector databases, fine-tunes models for specific domains, designs LLM agents with tool calling, evaluates model outputs, and deploys LLM-powered applications. Routine responsibilities include Python scripting, prompt engineering, retrieval evaluation, API integration, and using frameworks like LangChain to connect models, data and tools into coherent applications.

LLM Engineering vs Generative AI vs Data Science

LLM Engineering revolves around constructing, fine-tuning and deploying LLM-powered applications, while Generative AI & Agentic AI stresses autonomous agents, and Data Science prioritises predictive modelling.

Aspect LLM Engineering Generative AI Data Science
Core focus Building, fine-tuning & deploying LLM-powered applications Building GenAI apps + autonomous AI agents/multi-agent systems Predictive modelling from data
Coding depth High Moderate to High High
Typical entry salary ₹6–10 LPA (Entry-Level LLM Engineer) [CONFIRM per GenAI course sheet] ₹4–8 LPA (per Data Science course sheet)
Uncodemy program duration 5–6 months ~3–4 months (indicative) 8–9 months

Salary ranges are indicative and vary by role, skills, experience, location and employer.

Course Overview

The LLM Engineering course is a 5–6 month live online program delivered across 100+ live sessions and 160+ hours of learning. It is structured in 13 modules that progress from Generative AI foundations through Python for LLM engineering, ML & NLP fundamentals, transformer architecture, prompt engineering, LLM APIs & SDKs, embeddings & vector databases, RAG, LLM application development, fine-tuning, LLM agents & tool calling, evaluation & deployment, and a 20-hour Enterprise LLM capstone. Every module includes practical exercises with real tools, and the program closes with hands-on projects, RAG applications and an enterprise capstone. The program is delivered fully live online for Mangaluru and nationwide learners.

Who should join this course:

  • Developers wanting to specialise in LLM-powered applications and generative AI.
  • Data scientists and ML engineers upskilling into LLM engineering and RAG pipelines.
  • Aspiring AI engineers looking to build production-ready LLM applications.
  • Working professionals moving into GenAI/LLM roles from software or analytics backgrounds.
  • Mangaluru-based learners who want instructor-led live online training with hands-on projects.

Eligibility: Programming fundamentals are helpful but not mandatory — Python is taught from Module 2 onward. Comfort with basic ML/NLP concepts is a plus but built up in Module 3. A laptop with stable internet is required for practical sessions.

Upcoming LLM Engineering Course Batches for Mangaluru Learners

Choose a batch timing that suits your routine. Live online batches, IST timings. Book a free demo class first-hand, or join our upcoming weekend and weekday live batches — designed for both working professionals and 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 LLM Engineering Training Course in Mangaluru

LLM Engineering Course Syllabus in Mangaluru

The syllabus is organised into 13 modules covering 160 hours. The order follows how LLM engineering actually works: foundations → Python → ML/NLP basics → transformer architecture → prompt engineering → LLM APIs → embeddings/vector DBs → RAG → application development → fine-tuning → agents & tool calling → evaluation/deployment → capstone. Each module includes practical exercises with real tools, and the program closes with hands-on projects, RAG applications and an enterprise LLM capstone.

Module 1: LLM & Generative AI Foundations — 8 Hours

  • Introduction to large language models and generative AI
  • LLM architecture basics: parameters, context windows
  • Tokenization, embeddings and model behaviour
  • GenAI landscape: major model providers and use cases
  • How LLMs differ from traditional software systems
  • Probabilistic outputs and non-determinism in LLMs

Module 2: Python for LLM 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
  • Error handling and debugging for LLM applications
  • Async programming basics for API calls

Module 3: Machine Learning & NLP Fundamentals — 10 Hours

  • Supervised vs unsupervised learning basics
  • Training, validation and test splits
  • Text preprocessing and tokenization for NLP
  • Word embeddings and vector representations
  • Attention mechanisms and sequence modelling
  • Evaluation metrics for NLP tasks

Module 4: Transformers & LLM Architecture — 12 Hours

  • Transformer architecture: encoder, decoder, attention
  • Self-attention and multi-head attention
  • Positional encoding and context length
  • Pre-training vs fine-tuning paradigms
  • Model sizes, distillation and quantization
  • Hands-on: loading and inspecting transformer models

Module 5: Prompt Engineering & Structured Outputs — 8 Hours

  • Prompt design patterns and best practices
  • Zero-shot, few-shot and chain-of-thought prompting
  • Structured outputs: JSON, XML, function schemas
  • Prompt versioning and regression testing
  • Handling ambiguity and edge cases
  • Evaluating prompt reliability

Module 6: Working with LLM APIs & SDKs — 10 Hours

  • Overview of LLM APIs (OpenAI/Gemini/Claude-style)
  • Authentication, rate limits and error handling
  • Streaming responses and async calls
  • Function calling and structured output APIs
  • Cost management and token optimization
  • Building reusable API wrappers in Python

Module 7: Embeddings & Vector Databases — 10 Hours

  • Text embeddings and semantic similarity
  • Vector databases: FAISS, Chroma, Pinecone
  • Indexing strategies and performance trade-offs
  • Chunking strategies for documents
  • Metadata filtering and hybrid search
  • Hands-on: building a vector search pipeline

Module 8: Retrieval-Augmented Generation (RAG) — 15 Hours

  • RAG architecture: retriever, generator, context
  • Building end-to-end RAG pipelines with LangChain
  • Retrieval accuracy and relevance evaluation
  • Reranking and context compression
  • Handling multi-document and multi-hop queries
  • Hands-on: building a production-style RAG application

Module 9: LLM Application Development — 12 Hours

  • Designing LLM-powered applications
  • Chains, memory and context management
  • Integrating LLMs with external data sources
  • Building chat interfaces and APIs
  • Error handling and fallback strategies
  • Hands-on: building an LLM app with LangChain

Module 10: Fine-Tuning & Model Adaptation — 15 Hours

  • When to fine-tune vs prompt engineering vs RAG
  • Dataset preparation and formatting for fine-tuning
  • Parameter-efficient fine-tuning (LoRA, adapters)
  • Using Hugging Face Transformers for fine-tuning
  • Evaluating fine-tuned model performance
  • Hands-on: fine-tuning a small LLM on a custom dataset

Module 11: LLM Agents & Tool Calling — 15 Hours

  • Agent architecture: planning, tool use, memory
  • Tool calling and function execution
  • Multi-step reasoning and task decomposition
  • Building agents with LangChain
  • Agent evaluation and failure mode analysis
  • Hands-on: building a tool-using LLM agent

Module 12: LLM Evaluation, Optimization & Deployment — 15 Hours

  • Evaluation metrics for LLM outputs
  • Automated evaluation with LLM-as-judge
  • Observability and tracing for LLM applications
  • Latency, cost and throughput optimization
  • Deployment patterns: APIs, containers, serverless
  • Monitoring production LLM applications

Module 13: Enterprise LLM Projects & Capstone — 20 Hours

  • End-to-end LLM application design and architecture
  • RAG + fine-tuning + agents integration project
  • Deployment and evaluation of capstone application
  • Code review and portfolio preparation
  • Presentation and documentation of capstone project
  • Interview preparation around capstone work
# Module Hours Tools
1 LLM & Generative AI Foundations 8 ChatGPT, Gemini, Claude
2 Python for LLM Engineering 10 Python, Git
3 Machine Learning & NLP Fundamentals 10 Python, scikit-learn, NLTK
4 Transformers & LLM Architecture 12 Hugging Face Transformers
5 Prompt Engineering & Structured Outputs 8 ChatGPT, Gemini, Claude
6 Working with LLM APIs & SDKs 10 OpenAI/Gemini/Claude APIs, Python
7 Embeddings & Vector Databases 10 FAISS, Chroma, Pinecone
8 Retrieval-Augmented Generation (RAG) 15 LangChain, Vector DBs
9 LLM Application Development 12 LangChain, Python
10 Fine-Tuning & Model Adaptation 15 Hugging Face, LoRA
11 LLM Agents & Tool Calling 15 LangChain, Python
12 LLM Evaluation, Optimization & Deployment 15 LangSmith, Docker, Python
13 Enterprise LLM Projects & Capstone 20 All tools
Total 160 Hours 15+ tools

Contacted by

IR

Mr. Irshad

Curriculum & Technical Reviewer

[8+ Years Of Experience]

Specialization: LLM Engineering, Generative AI, RAG & Fine-Tuning

LinkedInProfile

Get Your LLM Engineering Certification In Mangaluru

Once you complete all 13 modules, the assignments and the Enterprise LLM capstone project, you will be awarded the Uncodemy LLM Engineering Program Certificate. This certificate notes the program name, duration, tools covered and completion date. It can be verified online at certificate.uncodemy.com and acts as a record of successful completion of the Uncodemy LLM Engineering program.

The certificate reflects the practical work completed during the program — including hands-on RAG applications, fine-tuning exercises and the 20-hour Enterprise LLM capstone project. Learners work with LLM APIs & SDKs (OpenAI/Gemini/Claude-style), LangChain, Vector Databases (FAISS/Chroma/Pinecone), Python, Hugging Face Transformers, prompt engineering tools, fine-tuning frameworks and evaluation/observability tools.

Key Benefits of Our LLM Engineering Certification:

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

This is an independent Uncodemy certificate. It is not affiliated with, endorsed by, or a credential of OpenAI, Google, Anthropic or any other LLM/vendor provider whose APIs 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 LLM Engineering Program Certificate
Uncodemy MSME registration certificate

Tools and Technologies Covered

Apply Now

Introducing Uncodemy

Uncodemy is a career-aligned IT training institute managed by Uncodemy Edutech Pvt. Ltd. With more than 14 years of training excellence, classroom centres in Delhi and Noida, and live online classes reaching learners across India, Uncodemy delivers instructor-led training for emerging technologies.

In this LLM Engineering program, learners build RAG applications, fine-tune models, design LLM agents with tool calling, and complete an enterprise LLM capstone project. The training covers how LLM-powered applications are engineered in production — prompt design, retrieval pipelines, model adaptation 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 Mangaluru learners

If you are still deciding on a direction, Uncodemy also runs Generative AI , Data Science and Playwright Automation Testing courses for Mangaluru and Karnataka learners.

Portfolio/capstone project review:Your hands-on RAG applications, fine-tuning work and enterprise LLM capstone are reviewed and refined into a portfolio an interview will find credible.

Resume & LinkedIn:We build your resume around LLM engineering keywords and measurable outcomes, and align your LinkedIn profile to match.

Interview preparation:System design for LLM applications, RAG pipeline debugging, fine-tuning trade-off questions and LLM 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
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

Uncodemy keeps two roles distinct that most institutes tend to blend together. Trainers conduct the live sessions. Domain reviewers — separate specialists who don't teach the batch — confirm 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. This arrangement means the individual teaching a course is not the only person verifying 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 LLM Engineering Training for Mangaluru Learners Today!

Sign up for Uncodemy's LLM Engineering course designed for Mangaluru learners today! Build practical skills in LLM APIs, RAG pipelines, fine-tuning and LLM agents through instructor-led live online training, hands-on projects and an enterprise LLM capstone. Begin your LLM engineering journey with structured training and career preparation.

LLM Engineer Jobs and Career Scope in Mangaluru+

Mangaluru is a growing technology and education hub, with a strong base of IT services, healthcare, banking, manufacturing and port-related industries. Many organisations in Kadri, Hampankatta, Surathkal and Baikampady are beginning to integrate LLM features — chatbots for customer support, RAG-based knowledge assistants, document-processing pipelines and agentic AI workflows — creating demand for engineers who understand LLM APIs, retrieval pipelines, fine-tuning trade-offs and deployment. Banking, healthcare, education, and logistics sectors in Mangaluru are also exploring GenAI, and they need engineers who can build reliable LLM-powered applications rather than only prototype them. Interviews in Mangaluru tend to weight system design for LLM applications, RAG pipeline design and debugging, and fine-tuning trade-offs most heavily. Candidates with software engineering or data/ML experience plus LLM engineering skills are well-positioned for these roles.

Job Roles After This Course+

LLM Engineer, AI Engineer, RAG/AI Application Engineer, Generative AI Engineer, ML Engineer (LLM-focused) and AI Solutions Architect (Junior) are the roles this course maps to. Each role involves building, fine-tuning or deploying LLM-powered applications, but they differ in the specific LLM components and tools used.
Role Core Tools
LLM Engineer LLM APIs, LangChain, Python, Vector DBs
AI Engineer Python, Hugging Face, RAG, Fine-Tuning
RAG/AI Application Engineer LangChain, Vector DBs, Embeddings
Generative AI Engineer LLM APIs, Prompt Engineering, Python
ML Engineer (LLM-focused) Hugging Face, Fine-Tuning, Evaluation Tools
AI Solutions Architect (Junior) LangChain, Deployment Tools, Python
Most graduates start as an LLM Engineer, AI Engineer or RAG/AI Application Engineer and move into the others with two to four years of experience, as they build depth in a specific LLM component or domain. Progression typically involves owning larger systems, leading projects, and specialising in fine-tuning, agents or deployment architecture.

LLM Engineer Salary in Mangaluru — Role-wise+

Salaries in Mangaluru vary by experience level, sector and how strong your LLM 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 LLM Engineer₹6–10 LPA
LLM Engineer / AI Engineer₹10–18 LPA
Mid-Level LLM Engineer₹15–25 LPA
Generative AI Engineer₹10–20 LPA
RAG / AI Application Engineer₹10–20 LPA
Senior LLM Engineer₹20–35 LPA
Lead AI / LLM Engineer₹25–45 LPA
Sources reviewed: AmbitionBox, Glassdoor, Indeed and Uncodemy placement records. Reviewed: September 2026.

Salary figures are presented as indicative market ranges, not guaranteed course 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 LLM Engineers in Mangaluru+

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 Mangaluru specifically, LLM engineering demand is emerging in:
  • IT services and global capability centres — Kadri, Hampankatta and Surathkal, integrating LLMs into enterprise applications
  • Product companies and SaaS startups — building chatbots, AI assistants and RAG-based features
  • Banking, financial services and insurance — LLMs for risk assessment, fraud detection and customer support
  • Healthcare and pharmaceuticals — LLMs for diagnostics support, documentation and patient engagement
  • Education, logistics and port-related industries — LLMs for content generation, tutoring and operational automation
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 an LLM Engineer — Step by Step+

Becoming an LLM 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.
  • ML/NLP basics— supervised learning, text preprocessing, embeddings.
  • Transformer architecture— attention, positional encoding, pre-training vs fine-tuning.
  • Prompt engineering— design patterns, structured outputs, evaluation.
  • LLM APIs & embeddings— API integration, streaming, vector databases.
  • RAG & application development— retrieval pipelines, LangChain, chat interfaces.
  • Fine-tuning, agents & deployment— LoRA, tool calling, evaluation and production deployment.
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 Mangaluru+

Mangaluru has several 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 hours160+
Live sessions100+
Tools15+
Fine-tuning moduleDedicated 15-hour module
Capstone20-hour Enterprise LLM project
Career supportDedicated Career & Placement Assistance
Compared to Uncodemy's Generative AI & Agentic AI course (~3–4 months, focused on GenAI apps and autonomous agents) and AI Testing course (1.5 months, focused on testing LLMs, RAG and AI agents), this LLM Engineering program is the deepest, longest and most engineering-heavy option — it includes dedicated fine-tuning and deployment modules plus a 20-hour capstone. If your goal is a full LLM engineering career transition, this is the right fit. If you only need prompt-engineering upskilling, a shorter course may suit you better. 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 LLM 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; Mangaluru 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 Mangaluru-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 LLM Engineering career transition This program
Quick prompt-engineering upskilling only Consider AI Testing or GenAI course
Already strong in traditional ML, want to specialise in LLMs This program
Building AI agents specifically Consider Generative AI & Agentic AI course

Frequently Asked Questions

1. What is the duration of the LLM Engineering course in Mangaluru?▼

The program covers a span of 5–6 months and includes 160+ hours of learning across 100+ live sessions. Both weekday and weekend batches are offered.

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

Familiarity with programming fundamentals is useful but not compulsory — Python is introduced from Module 2 onward. Basic ML/NLP concepts are established in Module 3. A laptop with stable internet connectivity is needed.

3. What tools are covered in this course?▼

15+ tools including LLM APIs & SDKs (OpenAI/Gemini/Claude-style), LangChain, Vector Databases (FAISS/Chroma/Pinecone), Python, Hugging Face Transformers, Prompt Engineering Tools, Fine-Tuning Frameworks, Evaluation/Observability Tools, and Git & GitHub.

4. Does the course include fine-tuning and LLM agents?▼

Yes. Module 10 covers Fine-Tuning & Model Adaptation (15 hours) and Module 11 covers LLM Agents & Tool Calling (15 hours). Both are core components with hands-on exercises.

5. What is the capstone project?▼

The capstone is Module 13: Enterprise LLM Projects & Capstone (20 hours). You build an end-to-end LLM-powered application applying RAG, fine-tuning, agents and deployment.

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

All 100+ sessions are live and instructor-led, with recordings available afterwards for revision.

7. What is the fee for the LLM Engineering course in Mangaluru?▼

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 LLM Engineer role after this course?▼

Entry-level LLM Engineer roles in the market range around ₹6–10 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 an LLM Engineer in Mangaluru?▼

Indicative market ranges: Entry-Level ₹6–10 LPA, Mid-Level ₹15–25 LPA, Senior ₹20–35 LPA, Lead ₹25–45 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?▼

LLM Engineer, AI Engineer, RAG/AI Application Engineer, Generative AI Engineer, ML Engineer (LLM-focused) and AI Solutions Architect (Junior).

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

Developers wanting to specialise in LLM-powered applications, data scientists/ML engineers upskilling into LLM engineering, aspiring AI engineers, and working professionals moving into GenAI/LLM 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 Mangaluru?▼

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

17. Is Uncodemy's LLM Engineering certificate affiliated with OpenAI, Google or Anthropic?▼

No. This is an independent Uncodemy certificate. It is not affiliated with, endorsed by, or a credential of OpenAI, Google, Anthropic or any other LLM/vendor provider whose APIs 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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The LLM Engineering course is delivered fully live online. Mangaluru 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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Delhi Centre
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
Free demo classWalk-ins welcomeWheelchair accessible
Delhi Map Noida Map
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