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Generative AI Engineer
Professional Certificate in Generative AI Engineer Program
We offer instructor-led online live virtual training and physical classroom training delivered by certified trainers and experienced industry professionals
- Get Up to 25% discount
Spearhead Your Career in Generative AI Engineer from CloudNet, Kolkata
Generative AI Engineer – Professional Program
Course Overview
The Generative AI Professional Program at CloudNet, Kolkata is an intensive, industry-oriented training designed to prepare learners for high-growth careers in Generative AI, Large Language Models (LLMs), AI Application Development, and Intelligent Automation.
This 6-month program covers Python for AI, Machine Learning fundamentals, Deep Learning, Generative AI models, Prompt Engineering, LLM fine-tuning, AI APIs, and real-world GenAI application development using industry-standard tools such as OpenAI, Hugging Face, LangChain, PyTorch, TensorFlow, and Vector Databases.
The course emphasizes hands-on labs, real-world projects, capstone development, and placement-oriented skill building, making it ideal for students and professionals aiming to work as Generative AI Engineers, AI Developers, or LLM Application Engineers.
Who Can Join This Course
This program is suitable for:
- Final-year students & graduates (B.Tech / BCA / MCA / B.Sc / M.Sc / BE – any discipline with basic math & logic)
- Working professionals from IT, software development, data analytics, testing, or automation backgrounds
- Python developers looking to transition into AI & GenAI roles
- Data Analysts / ML Engineers upgrading to Generative AI & LLM expertise
- Startup founders & product developers building AI-powered applications
- Career switchers aiming to enter AI & emerging technology roles
No prior AI experience required – fundamentals are covered from scratch.
Key Skills You Will Learn
- Python programming for AI & ML
- Machine Learning & Deep Learning foundations
- Generative AI concepts & architectures
- Prompt Engineering for LLMs
- Large Language Models (LLMs) – GPT, LLaMA, Mistral, Gemini
- Text, image & code generation models
- AI application development using APIs
- RAG (Retrieval Augmented Generation) systems
- Vector databases & embeddings
- Model fine-tuning & deployment basics
Why To Enroll for CloudNet India, Generative AI Training in Kolkata?
Course Duration & Format
- Duration: 6 Months
- Training Mode: Instructor-led classroom / live online
- Learning Style: Practical labs + real projects
- Assignments: Weekly hands-on tasks
- Capstone Project: Mandatory
Career & Job Roles After Completion
- Generative AI Engineer
- AI Application Developer
- LLM Engineer
- Prompt Engineer
- AI Research Assistant
- Machine Learning Engineer (GenAI Focus)
- AI Solutions Consultant
Placement Support by CloudNet, Kolkata
CloudNet provides placement-oriented support, including:
- Resume & LinkedIn profile preparation
- Interview preparation (technical + HR)
- Mock interviews & aptitude training
- Portfolio & GitHub project guidance
- Placement assistance with hiring partners
- Internship & entry-level role support
Placement support is subject to student performance, attendance, and market demand.
Certification
- CloudNet Certified Generative AI Professional
Industry-aligned project certification
Why Choose CloudNet, Kolkata
- Industry-experienced AI trainers
- Practical & job-oriented syllabus
- Real-world GenAI projects
- Small batch focused mentoring
- Dedicated placement support
Trusted IT training institute in Kolkata
Key Features
You will get 100% job Assurance and life time e-placement support
classed taken by globally certified trainers
You will get 3 year Dedicated placement support
Courses are globally recognized & accredited
Course Type:
Diploma Training Program
Live virtual classroom:
43,000/-
55,000/-
Regular classroom:
43,000/-
55,000/-
- Duration:
9 Months
- Enrolled:
47 Learners
- Eligibility:
10+2 / Any Graduate (BCA, B.Tech, B.Sc. IT)
- 5 Star:
17 Reviews
Module - I
Full Course Syllabus
Module 1: Python Programming for AI (Foundation)
- Python fundamentals & advanced concepts
- Data structures, functions, OOP concepts
- NumPy, Pandas, Matplotlib, Seaborn
- Data handling & preprocessing
- Python for automation & AI workflows
Module 2: Machine Learning Fundamentals
- Introduction to Machine Learning
- Supervised & Unsupervised Learning
- Regression, Classification, Clustering
- Model evaluation & performance metrics
- Scikit-Learn implementation
- Hands-on ML mini projects
Module 3: Deep Learning Essentials
- Neural networks fundamentals
- Backpropagation & optimization
- Deep learning frameworks: TensorFlow & PyTorch
- CNNs, RNNs, Transformers basics
- Practical deep learning model building
Module 4: Introduction to Generative AI
- What is Generative AI?
- Generative models overview
- Text, image, audio & code generation
- Use cases in business & industry
- Ethical AI & responsible AI practices
Module 5: Large Language Models (LLMs)
- LLM architecture & transformers
- GPT, BERT, LLaMA, Mistral overview
- Tokenization & embeddings
- Fine-tuning vs prompt-based learning
- LLM performance evaluation
Module 6: Prompt Engineering
- Prompt design principles
- Zero-shot, few-shot & chain-of-thought prompting
- Prompt optimization techniques
- Role-based prompts & system prompts
- Prompt engineering for chatbots & applications
Module 7: GenAI Tools & Frameworks
- OpenAI API & alternatives
- Hugging Face models & pipelines
- LangChain fundamentals
- LlamaIndex basics
- Vector databases (FAISS, Pinecone, ChromaDB)
Module 8: Retrieval Augmented Generation (RAG)
- Concept of RAG systems
- Embeddings & semantic search
- Document ingestion & indexing
- Building enterprise-grade Q&A systems
- Use cases: chatbots, knowledge assistants
Module 9: AI Application Development
- Building GenAI-powered applications
- Chatbot development
- AI assistants & automation tools
- Web & API integration
- Streamlit / FastAPI basics
Module 10: Capstone Project & Real-World Use Cases
- End-to-end Generative AI project
- Industry-based problem statements
- Model integration & deployment basics
- Project documentation & presentation
- GitHub portfolio creation
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