Build the skills needed to design, develop, fine-tune, deploy and integrate modern Artificial Intelligence applications.
The Professional Certificate in AI/ML & LLM Engineering for Developers is an industry-oriented program designed for software developers, Python developers, full-stack developers, data engineers, AI enthusiasts and computer science students who want to build practical expertise in Machine Learning, Deep Learning, Natural Language Processing (NLP), Large Language Models (LLMs), Generative AI, Retrieval-Augmented Generation (RAG), AI Agents and MLOps.
The program takes learners through the complete AI engineering journey—from Python programming and mathematics for Machine Learning to developing ML models, deep learning applications, transformer-based LLMs, RAG systems, AI agents and production-ready AI applications.
You will gain hands-on experience with technologies and frameworks including Python, NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Hugging Face, LangChain, LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, FAISS, ChromaDB, Pinecone, FastAPI, Docker, Kubernetes, MLflow, AWS, Azure AI and Google Cloud Vertex AI.
What You Will Learn
By completing this program, you will learn how to:
• Build Machine Learning models and evaluate their performance
• Develop Deep Learning applications using TensorFlow and PyTorch
• Work with Natural Language Processing and transformer architectures
• Understand how Large Language Models work
• Run and benchmark open-source LLMs
• Fine-tune open-source LLMs using techniques such as LoRA, QLoRA and PEFT
• Build Retrieval-Augmented Generation applications using vector databases
• Develop AI agents with tool calling, memory and planning capabilities
• Build multi-agent AI systems
• Develop applications using Model Context Protocol (MCP)
• Deploy LLM and AI applications using APIs and cloud infrastructure
• Apply MLOps practices for AI application deployment
• Implement AI security, evaluation and responsible AI practices
These outcomes are directly aligned with the program’s stated learning outcomes and curriculum.
Professional Certificate in AI/ML & LLM Engineering for Developers
Original price was: ₹59,999.00.₹29,999.00Current price is: ₹29,999.00.
Master AI/ML, Generative AI, LLMs, RAG & AI Agent Development
Duration: 6 Months | Industry-Oriented Program
Program Curriculum
Python for AI Development
Build the programming foundation required for AI and Machine Learning development.
- Topics
- Advanced Python
- Object-Oriented Programming (OOP)
- Data Structures & Algorithms
- NumPy
- Pandas
- APIs
- Asynchronous Programming
- Git & GitHub
- Virtual Environments
- Hands-On Projects
- Build reusable Python libraries
- REST API integration
- Data processing pipelines
Mathematics for Machine Learning
Develop the mathematical foundation required to understand and build Machine Learning models.
- Topics
- Linear Algebra
- Probability & Statistics
- Calculus
- Optimization
- Gradient Descent
- Hands-On Project
- Implement Gradient Descent from scratch
Machine Learning
Learn the fundamental algorithms and techniques used to build predictive Machine Learning models.
- Topics
- Data Preprocessing
- Feature Engineering
- Regression
- Classification
- Clustering
- Decision Trees
- Random Forest
- Support Vector Machines (SVM)
- K-Nearest Neighbors (KNN)
- Naïve Bayes
- Model Evaluation
- Hyperparameter Tuning
- Scikit-learn
- XGBoost
- LightGBM
- Hands-On Projects
- Customer Churn Prediction
- Loan Approval Prediction
- House Price Prediction
Deep Learning
Understand neural networks and the deep learning architectures used in modern AI applications.
- Topics
- Neural Networks
- TensorFlow
- Keras
- PyTorch
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- LSTM
- GRU
- Attention
- Transformers
- Hands-On Projects
- Image Classification
- Sentiment Analysis
- Object Detection
Natural Language Processing (NLP)
Learn how AI systems process, understand and work with human language.
- Topics
- Text Cleaning
- Tokenization
- Embeddings
- Word2Vec
- GloVe
- TF-IDF
- Named Entity Recognition (NER)
- spaCy
- NLTK
- Hugging Face
- Hands-On Projects
- Text Classification
- Question Answering
Large Language Models (LLMs)
Understand the architecture and ecosystem behind modern Large Language Models.
- Topics
- Transformer Architecture
- Self-Attention
- Tokenization
- Embeddings
- Context Windows
- Llama
- Mistral
- Qwen
- Gemma
- Phi
- Hugging Face Ecosystem
- Hands-On Projects
- Run open-source LLMs locally
- Benchmark LLM models
Building & Fine-Tuning Your Own LLM
Learn the techniques used to adapt open-source Large Language Models for specialized applications.
- Topics
- Dataset Preparation
- Instruction Tuning
- Supervised Fine-Tuning
- LoRA
- QLoRA
- PEFT
- Quantization
- Model Compression
- DeepSpeed
- Accelerate
- Hands-On Project
- Fine-tune an open-source LLM
Retrieval-Augmented Generation (RAG)
Learn how to build AI applications that combine LLMs with external knowledge sources.
- Topics
- Embeddings
- FAISS
- ChromaDB
- Pinecone
- Milvus
- Chunking
- Hybrid Search
- Reranking
- Hands-On Projects
- Enterprise PDF Chatbot
- Knowledge Assistant
AI Agents
Learn to build intelligent AI agents capable of using tools, maintaining memory and executing multi-step tasks.
- Topics
- LangChain
- LangGraph
- CrewAI
- AutoGen
- OpenAI Agents SDK
- Tool Calling
- Memory
- Planning
- Multi-Agent Systems
- Hands-On Projects
- Coding Assistant
- SQL Agent
- Research Agent
Model Context Protocol (MCP)
Learn how AI applications can connect with external tools, databases and systems using MCP.
- Topics
- MCP Architecture
- MCP Servers
- MCP Clients
- Tool Registration
- Database Integration
- Browser Automation
- Hands-On Project
- MCP Business Assistant
MLOps & AI Application Deployment
Learn how to deploy and operate AI applications using modern cloud and MLOps technologies.
- Topics
- FastAPI
- Docker
- Kubernetes
- MLflow
- CI/CD
- AWS
- Azure AI
- Google Cloud Vertex AI
- Hands-On Project
- Deploy an LLM as a REST API
AI Security & Responsible AI
Learn essential practices for developing secure, reliable and responsible AI applications.
- Topics
- Prompt Injection Protection
- AI Guardrails
- PII Protection
- Hallucination Detection
- Governance
- Model Evaluation
Capstone Projects
Apply the concepts learned throughout the program by building end-to-end AI applications.
- Custom Fine-Tuned LLM
- Enterprise RAG Chatbot
- AI Coding Assistant
- Multi-Agent Business Automation
- AI Research Assistant
- Document Intelligence System
- Voice AI Assistant
- End-to-End Cloud Deployment
Tools & Technologies
- Python
- VS Code
- Jupyter Notebook
- Git
- NumPy
- Pandas
- Scikit-learn
- TensorFlow
- PyTorch
- Hugging Face
- LangChain
- LangGraph
- CrewAI
- AutoGen
- OpenAI Agents SDK
- FAISS
- ChromaDB
- Pinecone
- FastAPI
- Docker
- Kubernetes
- MLflow
- AWS
- Azure AI
- Google Cloud Vertex AI
Who Should Take This Course?
This program is designed for:
- Software Developers
- Python Developers
- Full-Stack Developers
- Data Engineers
- AI Enthusiasts
- Computer Science Students
It is particularly suited to learners who want to move beyond using AI tools and develop the technical capability to build AI/ML and LLM-powered applications.
Career Paths
The skills developed through this program can support career paths in areas such as:
- AI Engineering
- Machine Learning Engineering
- LLM Engineering
- Generative AI Development
- RAG Application Development
- AI Agent Development
- AI Application Development
- MLOps and AI Deployment
| Duration | Srat Date | End Date | Time | Holidays |
| 5 months | 15-Sep-26 | 01-Mar-27 | Everyday 8 PM to 10 Pm | All Saturdays and Sundays 2 October 2026 Friday Gandhi Jayanti 20 October 2026 Tuesday Mahanavami / Ayudha Puja 21 October 2026 Wednesday Vijayadashami 10 November 2026 Tuesday Deepavali 27 November 2026 Friday Kanakadasa Jayanti 25 December 2026 Friday Christmas 31 December 2026 Thursday New Year’s Eve 1 Jan 2027 Friday New Year’s Eve 14 January 2027 Friday Thursday New Year’s Eve 14 January 2027 Thursday Makara Sankranti 26 January 2027 Tuesday Republic Day |
12 reviews for Professional Certificate in AI/ML & LLM Engineering for Developers
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Kavya Rao –
“I appreciated the way the course moved from fundamentals to practical applications. It made the learning process much easier to follow.”
B.E. Computer Science Student
Ananya Iyer –
“I found the sessions clear and engaging. The structured learning path gave me more confidence in applying what I learned.”
B.Tech Computer Science Student
Aditya Patel –
“A comprehensive learning experience with a good focus on practical implementation. The assignments added significant value.”
Software Developer
Priya Nair –
“I liked the balance between concepts and hands-on learning. The examples made the topics easier to understand and apply.”
Software Developer
Neha Kapoor –
“A useful learning experience for anyone looking to build relevant skills. I liked the practical orientation and the range of topics covered.”
IT Project Professional
Rahul Verma –
“I joined to build stronger skills and found the curriculum helpful. The practical examples made the learning more meaningful.”
Full-Stack Developer
Meera Krishnan –
“The instructors explained the topics in an accessible way. The course encouraged me to explore the subject further.”
MCA Student
Vikram Singh –
“The practical approach was the best part for me. I was able to connect the concepts to situations I encounter in my studies or work.”
Software Engineer
Siddharth Das –
“The program helped me understand how the concepts can be applied in real projects. The hands-on work was a valuable part of the experience.”
Data Engineer
Rohan Mehta –
“The curriculum was relevant and well organized. The project work was particularly useful for putting the learning into practice.”
IT Professional
Sneha Reddy –
“The course covered several useful areas and gave me a stronger foundation. I especially appreciated the hands-on activities.”
Technology Professional
Aarav Sharma –
“The program gave me a structured understanding of the subject. The practical exercises helped me connect the concepts with real-world applications.”
Software Engineer