Chandan Mishra CTO
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As the Chief Technology Officer at Rubick AI, a pioneering retail tech company, I am at the forefront of creating scalable SaaS products that have garnered the trust of retail giants such as Walmart, eBay, Amazon, Flipkart, Myntra, Myer, and numerous other prominent ecommerce platforms. My journey in the tech world has been nothing short of remarkable, with a strong partnership with the French Embassy, through which I am educating seven distinguished institutes across the globe on the integration of artificial intelligence in sustainable fashion.

Prior to my current role, I was a part of the groundbreaking team at Greendeck, recognized by Techstars and Forbes as one of the top four AI startups in the UK. As the first employee after the three co-founders, I immersed myself in a myriad of technologies, from building AI models, Python programming, and web scraping, to backend development with the Node and Express stack. My expertise also spans across Elastic search query optimization, Queue systems, Automation with Airflow, CI/CD practices, and Kubernetes. My time at Greendeck was a journey of ownership, problem-solving, and continuous learning, culminating in our notable feature in Forbes for our innovative work in AI.

My expertise caught the attention of Google, where I played a pivotal role as an early user researcher for Google Kubernetes Engine and Hangouts. My journey also led me to Leean AI, a Ycombinator-backed company, where I spearheaded the development of a large language model for the HRMS space. My consultancy has touched various domains, aiding companies in team building, scalable technology solutions, AI product development, funding, and strategic planning.

My extensive collaboration with multiple US-based companies, including HiveSight, NVIDIA, Lambent Technology, and Deeper System, has enriched my understanding of the practical knowledge required for successful job hunting and industry navigation. My teaching philosophy is rooted in real-world problem scenarios and industrial examples, ensuring that my students are not just learning but are equipped to excel in their future endeavors. If you choose to learn with me, you're choosing a path of practical knowledge, industry insights, and a mentor who is invested in your success. I am here not just to teach, but to guide you towards achieving your full potential and landing the job of your dreams.

Subjects

  • Python Beginner-Expert

  • Project Management Beginner-Expert

  • Cloud Computing Beginner-Expert

  • Machine Learning Beginner-Expert

  • Data Science Beginner-Expert

  • Django Beginner-Expert

  • NLP (Neuro-linguistic programming) Beginner-Expert

  • Artificial intelligence Beginner-Expert

  • Flutter Beginner-Expert

  • Node js Beginner-Expert

  • Technology Management Beginner-Expert

  • ChatGPT Beginner-Expert

  • Large Language Models Beginner-Expert

Experience

  • CTO (Aug, 2023–Present) at Rubick AI
    With my leadership as the Head of Product and Technology Strategy at
    Rubick.ai, we achieved a remarkable transformation from an end-to-end e-commerce cataloging firm to becoming a leading force in product performance and intelligence. By effectively managing our talented team, we maintained an impressively low churn rate of below 5% while driving substantial growth, with technology revenue contributing to 85% of our total revenue. One of our most significant accomplishments was the development of eight cutting-edge Al models that automated the entire cataloging process, propelling us to establish valuable partnerships with renowned companies like Myntra, Amazon, Jio Mart, Nykaa, JustInTimes, and eBay. Our impact in the industry has been profound, solidifying Rubick.ai as a pivotal player in the market.
  • Head of Product & Technology (Apr, 2022–Aug, 2023) at Rubick AI
    With my leadership as the Head of Product and Technology Strategy at
    Rubick.ai, we achieved a remarkable transformation from an end-to-end e-commerce cataloging firm to becoming a leading force in product performance and intelligence. By effectively managing our talented team, we maintained an impressively low churn rate of below 5% while driving substantial growth, with technology revenue contributing to 85% of our total revenue. One of our most significant accomplishments was the development of eight cutting-edge Al models that automated the entire cataloging process, propelling us to establish valuable partnerships with renowned companies like Myntra, Amazon, Jio Mart, Nykaa, JustInTimes, and eBay. Our impact in the industry has been profound, solidifying Rubick.ai as a pivotal player in the market.
  • AI Consultant (Jan, 2022–Jun, 2022) at Zipteams
  • Knowledge Graph Architecture (Dec, 2021–Jun, 2022) at Insurance Gig
    Consultant
  • Leena AI (YC) (Oct, 2021–Jul, 2022) at Machine Learning Engineer
    During my time at Leena Al, I led the development of a flagship HRMS knowledge-base product for enterprise and public companies. My main focus was on building generative Al models to automate interactions within the knowledge-base. I successfully worked on various models, including vector search, QA builders, intent classification, and matching, as well as PDF segmentation and training data virtualization. These efforts were aimed at empowering existing products with Al-powered automation, enhancing efficiency and effectiveness for our clients.
  • Founder In Residence (Sep, 2021–Dec, 2021) at Entrepreneur First
    Selected among top 50 startup in India
  • Head of Engineering (Mar, 2021–Dec, 2021) at textmercato
    I gradually led the engineering department in shifting the company's core focus from traditional technology to becoming an Al tech company. I spearheaded the vision of seamlessly integrating our enterprise and SaaS products with stacks of ML/Al tools for scalability and growth.
  • Cofondatore (Jan, 2021–Aug, 2022) at Togggle
  • Machine Learning Lead (Oct, 2020–Mar, 2021) at textmercato
    Built and nurtured a team of 20 talented individuals to increase automation in our enterprise products.
  • User Researcher (GKE, Hangout) (May, 2020–Aug, 2020) at Google
    User research on Google Kubernetes Engine and Hangout
  • Research and Development Engineer (Feb, 2020–Oct, 2020) at textmercato
    Started Al division in this 5-years old cataloging based company. Converted existing manual efforts to Al-based automated tools, those are reducing the workload of our operation team or else getting used as a dedicated service. With my team, I have built IR-based cataloging, text generation, Al-based HTML cataloging to help brands moving to a different marketplace, Al-based Image editing to support different marketplace image guidelines, Link sourcing, etc.Started Al division in this 5-years old cataloging based company. Converted existing manual efforts to Al-based automated tools, those are reducing the workload of our operation team or else getting used as a dedicated service. With my team, I have built IR-based cataloging, text generation, Al-based HTML cataloging to help brands moving to a different marketplace, Al-based Image editing to support different marketplace image guidelines, Link sourcing, etc.
  • ML Engineer (Jan, 2019–Jan, 2020) at Greendeck, Indore

Education

  • Startup Founder (Jan, 2022–Apr, 2022) from Y Combinator
  • MS in Artificial Intelligence (Jan, 2021–Jan, 2022) from Liverpool John Moores University
  • PG in Artificial Intelligence and Machine Learning (Dec, 2020–Jul, 2021) from International Institute of Information Technology Bangalore
  • B.Tech (Mar, 2015–Mar, 2019) from India Institute of Information Technology

Fee details

    ₹3,000–10,000/hour (US$35.68–118.92/hour)

    The cumulative fee or hourly rate may fluctuate based on the subject matter, the developmental phase, and tailored educational approaches.

Courses offered

  • Python A to Z

    • US$1000
    • Duration: 30 Days
    • Delivery mode: Online
    • Group size: Individual
    • Instruction language: English
    • Certificate provided: Yes
    Python A to Z is a comprehensive course designed to take students from the fundamentals to advanced concepts of Python programming. This course is meticulously crafted to cater to both beginners with no prior programming experience and experienced developers looking to enhance their skills in Python. Through a blend of theoretical knowledge and practical exercises, students will gain a deep understanding of Python and its applications in various domains.

    Course Syllabus:

    Week 1-2: Introduction to Python and Basic Concepts

    Introduction to Python programming
    Setting up Python and IDE installation
    Understanding variables, data types, and operators
    Exploring strings, lists, tuples, and dictionaries
    Control structures: if-else statements, loops
    Functions: definition, scope, arguments, and return values
    Practical exercises and assignments
    Week 3-4: Intermediate Python Concepts

    Deep dive into collections: sets, queues, and stacks
    Understanding file I/O
    Error and exceptions handling
    Modules and packages
    Working with dates and time
    Practical examples and real-world applications
    Assignments and mini-projects
    Week 5-6: Advanced Python Programming

    Introduction to Object-Oriented Programming (OOP)
    Understanding classes, objects, inheritance, and polymorphism
    Exception handling in depth
    Working with external libraries and APIs
    Introduction to regular expressions
    Advanced data handling with NumPy and pandas
    Mini-projects and assignments
    Week 7-8: Data Visualization and Web Scraping

    Introduction to data visualization with Matplotlib and Seaborn
    Basics of web scraping with BeautifulSoup and Scrapy
    Practical examples of data visualization and web scraping
    Assignments and projects
    Week 9-10: Databases and Web Development with Python

    Introduction to databases: SQL and NoSQL
    Connecting Python to a database
    Basics of web development with Flask/Django
    Building a basic web application
    Projects and assignments
    Week 11-12: Advanced Topics and Final Project

    Exploring advanced libraries and frameworks
    Introduction to asynchronous programming with asyncio
    Best practices in Python programming
    Code optimization and performance improvement
    Final project: Building a complete application using Python
    Project presentation and review
    Week 13: Conclusion and Next Steps

    Review of key concepts and learnings
    Guidance on how to continue learning and improving Python skills
    Q&A session
    Course feedback and closure

    Learning Outcomes:
    By the end of this course, students will have a strong grasp of Python programming, from basic syntax and structures to advanced concepts and applications. They will be equipped with the practical skills needed to solve real-world problems and will have a portfolio of projects to showcase their newfound expertise.

  • Machine Learning A to Z

    • US$2000
    • Duration: 45 Days
    • Delivery mode: Online
    • Group size: Individual
    • Instruction language: English
    • Certificate provided: Yes
    Machine Learning A to Z is a comprehensive course designed to take you from the foundational concepts of machine learning to mastering advanced techniques and applications. Whether you are a beginner eager to delve into the world of artificial intelligence or an experienced practitioner aiming to sharpen your skills, this course offers a structured and in-depth exploration of machine learning. By combining theoretical knowledge with practical exercises and real-world examples, you will gain a holistic understanding of how machine learning algorithms work, how to implement them, and how to make them work for you. By the end of this course, you will be well-equipped to apply machine learning techniques to real-world problems, drive innovations, and contribute to the rapidly evolving field of artificial intelligence.

    Syllabus:

    Week 1: Introduction to Machine Learning and Python

    Overview of Machine Learning
    Types of Machine Learning: Supervised, Unsupervised, Reinforcement
    Setting up the Python Environment
    Basics of Python Programming for Machine Learning
    Libraries Overview: NumPy, Pandas, Matplotlib, Scikit-Learn
    Week 2: Data Preprocessing and Visualization

    Handling Missing Values
    Encoding Categorical Data
    Feature Scaling
    Data Visualization Techniques
    Exploratory Data Analysis (EDA)
    Week 3: Regression Techniques

    Simple Linear Regression
    Multiple Linear Regression
    Polynomial Regression
    Support Vector Regression
    Decision Tree and Random Forest Regression
    Evaluation Metrics for Regression
    Week 4: Classification Techniques

    Logistic Regression
    K-Nearest Neighbors (KNN)
    Support Vector Machines (SVM)
    Kernel SVM
    Naive Bayes Classification
    Decision Trees and Random Forest Classification
    Evaluation Metrics for Classification
    Week 5: Clustering and Association

    K-Means Clustering
    Hierarchical Clustering
    Apriori Algorithm for Association Rule Learning
    Eclat Algorithm for Association Rule Learning
    Week 6: Natural Language Processing and Dimensionality Reduction

    Basics of Natural Language Processing (NLP)
    Text Cleaning and Preprocessing
    Implementing a Bag-of-Words Model
    TF-IDF Model
    Principal Component Analysis (PCA)
    Linear Discriminant Analysis (LDA)
    Week 7: Deep Learning and Neural Networks

    Introduction to Neural Networks
    Building an Artificial Neural Network
    Convolutional Neural Networks (CNNs)
    Recurrent Neural Networks (RNNs)
    Introduction to TensorFlow and Keras
    Week 8: Model Evaluation, Selection, and Boosting

    K-Fold Cross Validation
    Grid Search
    XGBoost
    Handling Imbalanced Datasets
    Saving and Loading Machine Learning Models
    Week 9: Advanced Topics and Industry Applications

    Introduction to Reinforcement Learning
    Overview of Unsupervised Deep Learning Models
    Machine Learning in Industry: Case Studies
    Ethics in Machine Learning
    Future Trends in Machine Learning
    Week 10: Final Project and Course Wrap-Up

    Guidelines for the Final Project
    Working on the Final Project
    Presentation of Projects
    Course Feedback and Next Steps
    Additional Resources:

    Access to a community forum for discussion and queries
    Weekly Q&A sessions
    List of further readings and resources for each topic
    This syllabus aims to provide a balanced mix of theory and practical application, ensuring that students not only understand machine learning concepts but also know how to implement them in real-world scenarios.

  • Generative AI A to Z

    • ₹3000
    • Duration: 60 Days
    • Delivery mode: Online
    • Group size: Individual
    • Instruction language: English
    • Certificate provided: Yes
    "Generative AI A to Z" is a comprehensive course designed to take students on a journey from the basics to advanced applications of generative artificial intelligence. Whether you're a beginner eager to dive into the world of AI or an experienced professional looking to deepen your understanding of generative models, this course is tailored for you. The curriculum is meticulously crafted to cover a wide array of topics, ensuring a holistic understanding of Generative AI, its underlying principles, applications, and ethical considerations. By integrating cutting-edge technologies such as LLM (Large Language Models), GPT (Generative Pre-trained Transformer), and ChatGPT, the course offers a balanced blend of theoretical knowledge and practical skills.

    Syllabus:

    Module 1: Introduction to Generative AI

    Week 1: Understanding AI and Machine Learning
    Week 2: Overview of Generative Models
    Week 3: Introduction to Neural Networks and Deep Learning
    Week 4: Foundations of Generative AI
    Module 2: Dive into Generative Models

    Week 5: Overview of Variational Autoencoders (VAEs)
    Week 6: Deep Dive into Generative Adversarial Networks (GANs)
    Week 7: Exploring Restricted Boltzmann Machines (RBMs)
    Week 8: Case Studies: Real-world Applications of Generative Models
    Module 3: Advanced Generative Techniques

    Week 9: Transfer Learning and Fine-tuning in Generative AI
    Week 10: Anomaly Detection with Generative Models
    Week 11: Image-to-Image Translation and Style Transfer
    Week 12: Text Generation with RNNs and Transformers
    Module 4: Large Language Models (LLM) and GPT

    Week 13: Introduction to Large Language Models and Transformers
    Week 14: Deep Dive into GPT: Architecture and Training
    Week 15: Practical Applications of GPT in Various Domains
    Week 16: Customizing and Fine-tuning GPT for Specific Use Cases
    Module 5: ChatGPT and Conversational AI

    Week 17: Understanding the Mechanics of ChatGPT
    Week 18: Building Conversational Bots with ChatGPT
    Week 19: Advanced Techniques and Best Practices in Conversational AI
    Week 20: Ethical Considerations and Future of Conversational AI
    Module 6: Real-world Projects and Capstone

    Week 21-23: Guided Projects: Applying Generative AI in Real-world Scenarios
    Week 24: Capstone Project: Ideation, Development, and Presentation
    Module 7: Future Trends and Closing Thoughts

    Week 25: Exploring Future Trends in Generative AI
    Week 26: Ethical Considerations and Responsible AI
    Week 27: Course Recap and Next Steps in Your Generative AI Journey
    Through a blend of lectures, hands-on labs, real-world projects, and interactive discussions, students will gain a robust understanding of Generative AI, empowering them to innovate and excel in this exciting field.

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