Ayush Rakesh Saxena

Ayush Saxena is an AI and data science professional with expertise in machine learning, generative AI, data analytics, and business intelligence. He holds an M.Tech in Data Science from BITS Pilani and has hands-on experience in building end-to-end AI systems, LLM pipelines, and RAG-based architectures. 

His technical expertise includes LangChain, Hugging Face, OpenAI APIs, Pinecone vector databases, and fine-tuning techniques such as QLoRA and PEFT, along with deep learning models including CNNs, RNNs, and GANs. 

With industry experience across analytics, marketing technology, and AI engineering, he brings a practice-oriented, business-focused approach to teaching, enabling students to apply AI and data-driven decision-making in real-world contexts.

Mr Saxena has worked across data science, analytics, and AI engineering roles, including positions as Growth Consultant, Data Analyst, and AI/ML Engineer. 

He has contributed to organisations such as Sprinto, Client Curve, RuDe Lab, and OYO, delivering projects in data modelling, dashboarding, SEO analytics, and business strategy, combining analytical insights with practical problem-solving.

Artificial intelligence; machine learning; data science; generative AI; NLP and LLMs; retrieval-augmented generation (RAG); applied analytics; AI-driven business decision-making.

His teaching focuses on conceptual clarity and hands-on application. Through problem-based learning, he encourages students to translate theory into data-driven solutions using real datasets and end-to-end AI workflows. 

He promotes a learning-by-doing approach, enabling students to build technical fluency, analytical thinking, and business problem-solving capabilities.

Python for Data Analytics; Text Mining; Business Analytics; Business Modelling with Spreadsheets.

Completed advanced certifications in Generative AI and LLM systems, including Databricks Fundamentals of Generative AI and NVIDIA’s RAG Agents with LLMs (in progress). 

Mr Saxena has developed multiple AI-driven applications and analytical systems, including RAG-based chatbots, hybrid search engines, and fine-tuned LLM models such as Llama-2 and BERT. 

His work also includes predictive analytics models, Tableau dashboards, and deep learning applications such as image generation using GANs. 

He actively explores the integration of generative AI, applied machine learning, and business analytics into pedagogy, with a focus on enhancing student readiness for AI-driven roles.