Become a Production-Ready AI Application Developer

Build applications,

Integrate LLMs,

Create APIs, Deploy AI systems, Handle backend logic, Manage vector databases

What You'll Learn

Gain in-demand Data Science and Machine Learning skills through real-world projects and hands-on training.

Master the art of building Machine Learning models in Python using essential libraries like NumPy and Scikit-learn.

Identify and apply the most suitable Machine Learning algorithm for various real-world business problems.

Understand how to clean, organize, and prepare data for accurate model training and deployment.

Master SQL and database management techniques for efficient data extraction, transformation, and manipulation.

Develop strong skills in Data Analytics and Business Intelligence (BI) tools, such as Power BI or Tableau, for creating interactive dashboards.

Create a strong portfolio of end-to-end projects that combine Data Science, Analytics, and AI to enhance your career profile.

Dive into advanced specialized domains including Deep Learning, NLP (Natural Language Processing), and Reinforcement Learning.

Implement effective Data Governance and Data Quality protocols to ensure reliable, ethical, and compliant data practices in professional environments.

Visualize and interpret massive datasets using popular libraries like Matplotlib and Seaborn to communicate insights effectively.

Utilize cloud computing platforms (e.g., AWS, Azure, or GCP) to deploy and manage scalable Machine Learning models and Big Data workflows.

Explore the fundamentals of Data Engineering and understand the use of big data tools like Hadoop, Spark, and Kafka.

Master soft skills essential for data professionals, including technical communication, stakeholder management, and translating business needs into analytical requirements.

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LLM Engineer

An LLM Engineer is a professional who can:

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AI research assistants
⚑
Enterprise knowledge chatbots
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AI copilots
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Automated document intelligence
✍️
AI customer support systems
🌍
AI workflow automation

They bridge the gap between AI research models and production AI systems.

Why This Role Is Exploding in 2026

AI is rapidly moving from research labs into real-world products. Every modern SaaS company is integrating AI features, creating massive demand for engineers who can build production-ready AI applications.

AI Resume Analyzer
AI Document Summarizer
AI Chatbots & Assistants
AI Code Generators
AI Marketing Automation Tools

Who Is This For?

Software Developers
Backend Engineers
Full Stack Developers
Machine Learning Engineers
Data Scientists

Skills Covered

Large Language Model Fundamentals
Advanced Prompt Engineering
Retrieval-Augmented Generation (RAG)
LLM Evaluation & Optimization
Fine-Tuning & Open-Source Models
AI Agents & Tool Calling

Tools & Technologies Used

Python
LLM APIs
Vector Databases
Embedding Models
Open-Source LLMs
RAG Architecture

Job Roles After Completion

LLM Engineer
Generative AI Engineer
AI Systems Engineer
Applied AI Engineer

Salary Expectations

Entry Level

β‚Ή12–18 LPA

Mid Level

β‚Ή20–35 LPA

Senior LLM Engineer

β‚Ή40+ LPA

Mentorship & Learning Model

Small live batches
Live coding sessions
Code reviews & feedback
Real-world portfolio projects
Career guidance & resume prep
Interview preparation support

Course Content

Comprehensive curriculum designed by industry experts

Module 1 β€” Python
  • Introduction
  • String manipulation
  • Data structures
  • Control loops
  • Functions
  • Object oriented programming
  • Modules and packages
  • Graphical user interface (GUI)
  • Exception & file handling
  • Variables
  • Pep8
  • Advanced concepts
  • API & Project
  • Introduction to Data Science
  • Introduction to Machine Learning
  • Common charts used.
  • Inferential Statistics.
  • Probability, Central Limit theorem, Normal Distribution & Hypothesis testing.
Β 
  • Introduction to Machine Learning (ML)
  • Statistics for Data Science
  • Data Visualization
  • Exploratory Data Analysis
  • Data reprocessing
  • Linear regression
  • Logistic Regression
  • Decision trees & random forests
  • Model evaluation techniques
  • Dimensionality reduction using PCA
  • KNN (K–Nearest Neighbours)
  • Naive Bayes Classifier
  • K-means clustering technique
  • Support Vector Machines (svm)
  • Time series forecasting
  • Ensemble learning
  • Stacking
  • Optimization
  • Capstone Projects
  • Machine Learning workflow.
  • Artificial Neural Networks.
  • The Activation Function.
  • Building an ANN.
  • Convolutional Neural Networks.
  • Pooling and Flattening.
  • Recurrent Neural Networks.
  • RNN Intuition.
  • The Vanishing Gradient Problem.
  • Inferential Statistics.
  • Data visualization with Matplotlib.
  • Encoding categorical variables.
  • Logistic Regression.
  • Building an AutoEncoder.
  • Data reprocessing.
  • Large Language Models (LLMs).
  • LLM’s Industry use cases.
  • Prompting Techniques.
    • One Shot Prompting.
    • FewS hot Prompting.
  • OpenAI – GPT 3.5 / GPT 4.
  • LangChain.
  • Hugging Face.
  • Google PaLM
  • Google Gemini / Gemini Pro / Gemini Pro Vision.
24
  • Large Language Models (LLMs).
  • LLM’s Industry use cases.
  • Prompting Techniques.
    • One Shot Prompting.
    • FewS hot Prompting.
  • OpenAI – GPT 3.5 / GPT 4.
  • LangChain.
  • Hugging Face.
  • Google PaLM
  • Google Gemini / Gemini Pro / Gemini Pro Vision.
  • Identity & Access Management (IAM)
  • Simple Storage Service (S3)
  • Networking
  • Elastic Compute Cloud (EC2) – I
  • Elastic Compute Cloud (EC2) – II
  • Database services
  • Individual services
  • Route53
  • Managed application services
  • Analytics applications
  • Capstone projects
  • Introduction to MySQL
  • Inserting data
  • Crud commands
  • String functions
  • Basic database terminology
  • Mysql constraints
  • Aggregate functions
  • MySQL stored procedure – I
  • MySQL stored procedure – II
  • Tableau basics
  • Maps, scatterplots & your first dashboard
  • Joining and blending data, plus: dual axis charts
  • Table calculations, advanced dashboards, storytelling
  • Advanced data preparation
  • Introduction
  • Connecting & shaping data with Power BI desktop
  • Creating table relationships & data models in Power BI
  • Analyzing data with dax calculations in Power BI
  • Visualizing data with Power BI reports
  • Artificial Intelligence (AI) visuals
  • Customer Segmentation
  • Recommender System
  • Build a Chatbot
  • Customer Segmentation
  • Recommender System
  • Build a Chatbot
  • Customer Segmentation
  • Recommender System
  • Build a Chatbot
  • Customer Segmentation
  • Recommender System
  • Build a Chatbot
  • Customer Segmentation
  • Recommender System
  • Build a Chatbot

Capstone Projects

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Intelligent Resume Screener using NLP + RAG

Natural Language Processing + Retrieval-Augmented Generation

  • Β Build an NLP pipeline to extract skills, experience, and education from resumes.
  • Β Create a vector database (FAISS) of job descriptions.
  • Β Use a RAG model to match candidate resumes to job postings dynamically.
  • Output: Ranking of best-fit candidates and a Chatbot interface for HR queries.

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Customer Segmentation & Targeted Marketing Engine

Clustering + Consumer Behavior Analytics

  • Predict demand for products using ML regression models.
  • Apply Reinforcement Learning to optimize inventory restocking & routing.
  • Simulate supply chain operations and reduce cost per delivery.
  • Output: Dynamic RL-powered supply chain dashboard.

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AI-powered Fraud Detection System

Machine Learning + Anomaly Detection

  • Train supervised + unsupervised models on financial transaction datasets.
  • Use anomaly detection for new fraud patterns.
  • Build a dashboard showing fraud probability and visual transaction clusters.
  • Output: Real-time fraud alerting web dashboard.

⚑

Energy Consumption Prediction and Optimization

Time Series Forecasting + Predictive Analytics

  • Collect smart meter energy consumption data.
  • Build a forecasting model usingΒ  LSTM / Prophet to predict energy demand.
  • Suggest optimal load distribution or appliance usage.
  • Output: Real-time energy dashboard with recommendations for saving electricity.

🚦

Smart Traffic Violation Monitoring using Computer Vision

Deep Learning + Object Detection

  • Detect traffic rule violations (no helmet, red light jump, wrong lane) using real CCTV footage.
  • Integrate OCR for license plate recognition.
  • Send auto-generated violation reports.
  • Output: Intelligent traffic surveillance and automated penalty system.

πŸ’°

AI Financial Advisor Agent

Multi-Agent System + Generative AI

  • Build AI Agents that perform different roles: Market Analyst, Risk Evaluator, and Portfolio Rebalancer.
  • Agents collaborate autonomously to provide personalized investment advice.
  • Users can chat with the system for portfolio insights.
  • Output: Autonomous, explainable AI-driven portfolio recommendations.

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Retail Demand Forecasting & Assortment Optimization Platform

Time-series forecasting + assortment optimization + data-driven merchandising

  • Collect and clean sales, pricing, promotion, and inventory data.

  • Build SKU-level demand forecasting models (Prophet / LightGBM).

  • Recommend optimal inventory & assortment using optimization algorithms.

  • Deploy a dashboard showing forecasts, stock actions, and KPIs.

πŸ“‰

Customer Churn Prediction & Retention Engine

Predictive Modeling + Explainable AI

  • Train ML model (XGBoost / Random Forest) to predict churn probability.

  • Use SHAP explainability to show factors leading to churn.

  • Build a Retention Recommender (discount, callback, plan upgrade).

  • Output: Interactive dashboard showing churn risk, reasons, and recommended action plan.

πŸ“Š

AI-driven Marketing Campaign Performance Analyzer

Analytics + NLP Insights + Optimization

  • Use time-series forecasting to predict conversions & ROAS.
  • Apply NLP to evaluate ad text quality & sentiment.

  • Automatically detect performing vs. non-performing campaigns.

  • Output: Smart dashboard recommending scaling/pausing campaigns + creative suggestions.

Things You Should Know

Important notes, tips and expectations for MeulTech learners β€” short, clear and action-oriented.

one
Course Projects are Mandatory
Every program includes capstone projects. These are essential to pass and are heavily weighted in placement assistance.
two
Attendance & Live Sessions
Live sessions build practical skills. Aim for 80% attendance to receive priority interview calls.
three
Placement Support Details
Placement includes resume review, mock interviews and job referrals. Final placement depends on performance and availability.
four
Flexible Payment Options
EMI and instalment plans available. Scholarships offered for meritorious learners β€” contact support for eligibility.

Start your Dream Career with a 3-Month Integrated Internship Program

Course Certification

Complete your comprehensive training withΒ MeulTechΒ and earn the prestigious

Certified Data Science & AI Professional

This official certification is your professional proof and a clear indicator of your full expertise in Data Science, Analytics, and AI, designed to accelerate your career growth.

Why Learners Choose Us

Learn, build, and get hired β€” with expert-led, hands-on training

Industrial Expert Trainer

Get direct guidance from seasoned data scientists and AI specialists actively working in top tech companies β€” their real-world experience forms the core of your learning.

01

100% Practical Training

We prioritize hands-on application over lectures. You'll gain tangible skills by coding, building models, and solving problems using live, complex datasets.

02

Industry Oriented Capstone Projects

Create end-to-end capstone projects that simulate real business challenges β€” a portfolio that proves your expertise to recruiters.

03

Global Certification

Earn a valuable Certified Data Science & AI Professional credential β€” an official certificate that boosts your profile and validates your expertise worldwide.

04

Dedicated Practice Space with laptop

Benefit from a dedicated, well-equipped practice facility. We provide high-spec laptops and software during lab time, ensuring zero technical friction during study hours.

05

Personalized Instructor Support

Clarify complex concepts immediately with our one-on-one doubt-solving sessions β€” trainers committed to your personalized understanding.

06

Future-Proof, Dynamic Curriculum

Our syllabus is updated with the latest in Generative AI and MLOps. Learn modern techniques using cutting-edge AI tools and methodologies.

07

ATS Based Resume Building

Receive coaching to craft an ATS-optimized resume and refine your LinkedIn profile β€” make sure your applications pass screening software and capture recruiter attention.

08

Linkedin Profile Building

Receive coaching to craft an ATS-optimized resume and refine your LinkedIn profile. Maximize your visibility to recruiters and capture their immediate attention.

09

What Can You Become?

Career roles after completing the Digital Marketing course

Data Scientist

Data Analyst

Machine Learning Engineer

Data Engineer

Data Science and Data Analytics with AI

Deep Learning Specialist

Data Visualization Expert

Big Data Analyst

Predictive Analytics Specialist

Turn your Data Science and Data Analytics with AI skills into a career β€” pick a role and build projects for your portfolio.

Frequently Asked Questions

Do I need AI background?
Basic Python and programming knowledge is required. AI theory will be taught from scratch.
Is this theoretical?
No. 80% hands-on engineering.
Will I build real LLM systems?
Yes. You will deploy production-grade AI systems.