AI Engineering

Build job-ready skills through hands-on experience

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AI Engineering:

As a Generative AI Engineer, you'll master the design and deployment of AI systems that create text, images, code, and video using state-of-the-art transformers and large language models (LLMs). This will equip you with applied skills in GenAI, NLP, and machine learning using Python.

You’ll build and deploy real-world applications using tools like Hugging Face, PyTorch, LangChain, and models such as BERT, GPT, and Diffusion Models.

Through hands-on labs and projects, you will:

  • Generate human-like text, visuals, and functional code
  • Apply prompt engineering and in-context learning to control model behavior
  • Train and fine-tune custom LLMs
  • Build end-to-end GenAI applications using Flask, LangChain, and RAG (Retrieval-Augmented Generation)
  • Create portfolio-ready projects that demonstrate real-world GenAI capabilities
  • By the end, you’ll have practical experience and the technical foundation to stand out in roles focused on Generative AI, ML engineering, and AI product development.

  • Gain job-ready skills in gen AI, machine learning, deep learning, and NLP.
  • Build and deploy AI apps, agents, and chatbots using Python tools like Flask, PyTorch, and Scikit-learn.
  • Learn prompt engineering, model training, and fine-tuning with BERT, GPT, RAG, and LangChain.

Jupyter, Unit Testing, ChatGPT, Generative AI, Data Wrangling, Data Import/Export, Unsupervised Learning, Predictive Modeling, Prompt Engineering, Feature Engineering, Applied Machine Learning, Flask.

  1. Introduction to Artificial Intelligence 
  2. Generative AI: Introduction and Applications
  3. Generative AI: Prompt Engineering Basics
  4. Python for Data Science, AI & Development
  5. Developing AI Applications with Python and Flask
  6. Building Generative AI-Powered Applications with Python
  7. Data Analysis with Python
  8. Machine Learning with Python
  9. Introduction to Deep Learning & Neural Networks with Keras
  10. Generative AI and LLMs: Architecture and Data Preparation
  11. Gen AI Foundational Models for NLP & Language Understanding
  12. Generative AI Language Modeling with Transformers
  13. Generative AI Engineering and Fine-Tuning Transformers
  14. Generative AI Advance Fine-Tuning for LLMs
  15. Fundamentals of AI Agents Using RAG and LangChain
  16. Project: Generative AI Applications with RAG and LangChain

Training Objectives

Introduction to AI


  • Understand key AI concepts and real-world applications across domains
  • Learn and apply machine learning, deep learning, and neural networks
  • Explore how generative AI drives innovation and business transformation
  • Design ethical GenAI solutions to address organizational challenges

Gen AI: Introduction and Applications

  • Understand generative AI and how it differs from discriminative AI
  • Explore real-world use cases and industry applications
  • Learn common GenAI models and tools for text, code, image, audio, and video
  • Design ethical GenAI solutions for organizational challenges

Gen AI: Prompt Engineering

  • Understand the concept, relevance, and best practices of prompt engineering
  • Apply techniques to improve LLM output quality and reliability
  • Practice methods like interview pattern, chain-of-thought, and tree-of-thought
  • Explore popular tools that support effective prompt engineering

Python for Data Science, AI & Development

  • Learn Python fundamentals: syntax, data types, variables, and string operations
  • Apply logic with data structures, loops, functions, and object-oriented programming
  • Use Python libraries like Pandas and NumPy in Jupyter Notebooks
  • Access web data using REST APIs and web scraping with BeautifulSoup

Develop AI Applications with Python and Flask

  • Follow the Python application development lifecycle from planning to deployment
  • Write Python modules, run unit tests, and follow PEP8 best practices
  • Build and deploy web apps with Flask, including routing and CRUD operations
  • Develop and deploy AI-powered apps using IBM Watson and Flask

Build Generative AI-Powered Applications with Python

  • Understand core GenAI concepts, including LLMs, speech tech, and platforms like IBM watsonX and Hugging Face
  • Build AI apps and chatbots using LLMs, RAG, and Python frameworks
  • Integrate speech-to-text and text-to-speech for voice-enabled AI interfaces
  • Develop web-based AI apps with Flask, Gradio, HTML, CSS, and JavaScript

Data Analysis with Python

  • Clean and prepare data in Python by handling missing values, formatting, normalization, and binning
  • Perform exploratory data analysis (EDA) with Pandas, NumPy, and SciPy
  • Use dataframes to summarize distributions, analyze correlations, and build data pipelines
  • Build and evaluate regression models with Scikit-learn for predictive insights

Machine Learning with Python

  • Understand key ML concepts, tools, and roles, including supervised and unsupervised learning
  • Apply core algorithms like regression, classification, clustering, and dimensionality reduction
  • Evaluate models with metrics, validation methods, and optimization techniques
  • Build end-to-end ML solutions using real-world datasets and hands-on projects

Introduction to Deep Learning & Neural Networks with Keras

  • Understand the basics of deep learning, neurons, and neural networks
  • Explore core components of neural nets and training challenges
  • Build regression and classification models with Keras
  • Design advanced models like CNNs, RNNs, and transformers for real-world tasks

Generative AI and LLMs: Architecture and Data Preparation

  • Compare GenAI architectures: RNNs, Transformers, VAEs, GANs, Diffusion
  • Apply LLMs (GPT, BERT, BART, T5) to NLP tasks
  • Preprocess text with tokenization using NLTK, spaCy, and Transformers
  • Build NLP data loaders in PyTorch with tokenization and padding

Gen AI Foundational Models for NLP & Language Understanding

  • Convert text into numerical features using one-hot encoding, bag-of-words, embeddings, and embedding bags
  • Implement Word2Vec with CBOW and Skip-gram for contextual word embeddings
  • Train language models using N-Grams and feedforward neural networks
  • Build encoder–decoder RNNs for machine translation and sequence tasks

Generative AI Language Modeling with Transformers

  • Understand how attention captures context in transformer models
  • Compare decoder-based (GPT) vs. encoder-based (BERT) language models
  • Build transformer components (positional encoding, attention, masking) in PyTorch
  • Apply transformers to NLP tasks like classification and translation using Hugging Face

Generative AI Engineering and Fine-Tuning Transformers

  • Gain in-demand skills for transformer-based LLMs in generative AI
  • Learn parameter-efficient fine-tuning with LoRA and QLoRA
  • Fine-tune pretrained models for specific NLP tasks
  • Use Hugging Face and PyTorch to load, train, and run models

Generative AI Advance Fine-Tuning for LLMs

  • Develop in-demand generative AI skills in fine-tuning large language models (LLMs).
  • Apply instruction tuning, reward modeling, and RLHF techniques using Hugging Face.
  • Master direct preference optimization (DPO) to solve LLM fine-tuning challenges.
  • Use proximal policy optimization (PPO) for building scoring functions and tokenizing datasets.

Fundamentals of AI Agents Using RAG and LangChain

  • Master job-ready skills to build AI agents with RAG and LangChain in just 8 hours
  • Apply in-context learning and advanced prompt engineering to enhance prompt design
  • Learn key LangChain concepts: tools, components, chat models, chains, and agents
  • Build AI apps by integrating RAG, PyTorch, Hugging Face, LLMs, and LangChain

Generative AI Applications with RAG and LangChain

  • Build a real-world generative AI app to showcase in interviews
  • Create and configure a vector database for document embeddings
  • Develop a retriever to fetch relevant info from user queries
  • Set up a Gradio interface and build a Q&A bot using LangChain and LLMs