Learn Generative AI and Transition to Tier-1 Companies

Designed and taught by FAANG+ AI/ML Engineers to help you transform your career and land your dream job.

Customized learning modules for:

  • Software Engineers
  • Product Managers
  • Engineering Managers
  • Data Scientists/ML Engineers
  • Technical Program Managers

Live Online Classes | Interview Prep | 1:1 Career Support

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500+

Instructors, Coaches & Interviewers from Top Companies

20,000+

Students

66.5%

Avg. Salary Hike for Alums Who Upleveled

Tools you’ll learn

Interview Kickstart EdgeUp—Gain the AI Edge & Boost Your Career

Back-end Engineers

4.5

Front-end Engineers

4.6

Full-stack Engineers

4.7

Test Engineers

4.8

Product Managers

4.8

Engineering Managers

4.7

Technical Program Managers

4.7

Machine Learning Engineers

4.6

Data Scientists

4.6

Why EdgeUp?

Many companies have started adding Generative AI skills to job descriptions of several tech roles. We analyzed more than 10,000 JDs and found that over 10% of the roles now require GenAI skills, to be able to leverage the power of GenAI and improve productivity, efficiency, and creativity. Here are our findings:

GenAI-enabled Job Descriptions & skills

GenAI Skills Back-end Engineers Need Today
  • Python Proficiency: Understand Python and neural networks.
  • AI Integration: Leverage Generative AI in development.
  • Automation: Use AI for task automation.
  • Code Generation: Employ AI for coding.
  • Debugging: Enhance debugging with AI.
  • Testing: Improve testing via AI.
  • Innovation: Create novel AI-driven applications.
  • Feature Integration: Integrate AI features into tech stacks.
  • Architecture Knowledge: Understand AI architectures and types.
  • Project Planning: Stay relevant in AI project planning.
GenAI Skills Front-end Engineers Need Today
  • Python Basics: Understand Python and neural networks.
  • AI Integration: Utilize Generative AI in development.
  • Automation: Automate tasks with AI.
  • Code Generation: Leverage AI for coding.
  • Debugging: Enhance debugging processes.
  • Testing: Improve testing with AI.
  • UI/UX Innovation: Innovate user interfaces using AI.
  • Feature Integration: Integrate AI features into front-end projects.
  • Performance Optimization: Use AI to optimize performance.
  • Architecture Knowledge: Understand AI architectures and capabilities.
GenAI Skills Full-stack Engineers Need Today
  • Python Basics: Understand Python and neural networks.
  • AI Integration: Leverage Generative AI in development.
  • Automation: Automate tasks with AI.
  • Code Generation: Use AI for coding.
  • Debugging: Enhance debugging processes.
  • Testing: Improve testing efficiency.
  • Innovative Applications: Create novel AI-driven applications.
  • Feature Integration: Integrate AI features into tech stacks.
  • Performance Optimization: Optimize performance using AI.
  • Architecture Knowledge: Understand AI architectures and capabilities.
GenAI Skills Test Engineers Need Today
  • LLM Implementation: Test applications using LLMs.
  • Neural Network Knowledge: Understand various neural architectures.
  • Model Behavior: Identify model failure points.
  • Language Models: Grasp intricacies of language and generative models.
  • Transformers: Understand transformer mechanisms.
  • Scenario Creation: Generate diverse test scenarios with AI.
  • Regression Testing: Enhance regression testing efficiency.
  • Focus on Complex Tasks: Prioritize tasks requiring human intuition.
  • Model Size Knowledge: Understand model size and training data implications.
  • Scalability Monitoring: Test scalability and efficiency of AI models.
GenAI Skills Product Managers Need Today
  • AI Knowledge: Understand generative AI basics and limitations.
  • User Experience: Enhance experiences through automated content and personalized interactions.
  • Strategic Use: Deploy AI for competitive advantages and market opportunities.
  • Integration: Integrate AI in product development.
  • Communication: Collaborate effectively with technical teams.
  • Impact: Evaluate AI's effect on user interfaces.
  • Ethics: Ensure ethical AI use and compliance.
GenAI Skills Engineering Managers Need Today
  • Feasibility Assessment: Assess technical feasibility and ROI of GenAI projects.
  • Market Trends: Understand trends and competitive advantages.
  • Project Prioritization: Prioritize projects aligned with business goals.
  • Team Management: Break down AI features and assign tasks.
  • Challenge Understanding: Grasp AI implementation challenges and timelines.
  • Innovation Potential: Recognize GenAI's potential for problem-solving.
  • Project Lifecycle: Manage AI projects from data collection to deployment.
  • Technical Guidance: Provide technical guidance on GenAI projects.
  • Experimentation: Explore and run small-scale AI experiments.
  • Evaluation Skills: Evaluate LLMs and market offerings for build-vs-buy decisions.
GenAI Skills Technical Program Managers Need Today
  • Risk Management: Simulate and prepare for project risks with GenAI.
  • Risk Assessment: Use LLMs to assess project health and identify risk patterns.
  • Technical Understanding: Grasp GenAI fundamentals and technical challenges.
  • AI Planning: Plan AI models, timelines, and associated risks.
  • Experimentation: Conduct and convert AI experiments into production solutions.
  • Automation: Automate project scheduling, resource allocation, and task prioritization.
  • Routine Monitoring: Automate monitoring and reporting tasks.
  • Strategic Focus: Free up time for strategic planning.
  • Insight Generation: Generate GenAI-enabled insights for project adjustments.
  • Enhanced Accuracy: Improve analysis accuracy with AI.
GenAI Skills ML Engineers Need Today
  • Advanced Model Development: Create innovative AI models.
  • Problem Solving: Solve complex problems with GenAI.
  • State-of-the-Art Projects: Work on cutting-edge AI technologies.
  • Job Market Competitiveness: Enhance employability with advanced skills.
  • Efficiency: Improve workflows via automation and synthetic data generation.
  • Model Performance: Enhance models with fine-tuning and transfer learning.
  • AI-Driven Solutions: Develop AI that understands/generates text, images, audio.
  • Stay Current: Keep skills updated with latest GenAI advancements.
  • Resource Optimization: Reduce computational costs with AI optimizations.
  • Security and Ethics: Mitigate biases and vulnerabilities in models.
GenAI Skills Data Scientists Need Today
  • Model Innovation: Develop advanced AI models.
  • Automation: Automate data preprocessing and feature engineering.
  • Data Augmentation: Generate synthetic data for training.
  • Model Improvement: Enhance models with fine-tuning and transfer learning.
  • AI Solutions: Create AI-driven applications (chatbots, recommendation systems).
  • Productivity: Boost efficiency with AI-driven workflows.
  • Optimization: Optimize model performance and scalability.
  • Ethics and Security: Mitigate biases and vulnerabilities.
  • Decision-Making: Improve decisions with AI-driven insights.
  • Competitive Edge: Stay current with AI advancements for market competitiveness.

Detailed Curriculum

Back-end Engineers

Python Crash Course
Weeks 1 & 2
Hands-on with Generative AI
Weeks 3 & 4
Understanding Gen AI Architecture
Weeks 5 & 6
Building Applications & Use Cases
Weeks 7-10
Gen AI for Software Engineers Specialization & Capstone Projects
Weeks 11-14
AI Customer Service Agent
AI Personal Shopper
Data Structure & Algorithms
Weeks 15-19
Scalable System Design
Weeks 20-22
Database Design & Object Modelling
Weeks 23-24
API Design & Cloud Native Design
Weeks 25-26
Concurrency
Week 27
Career Sessions: Mock Interviews, Feedback Sessions
Weeks 28-30
Support Period
Week 31 onward

Front-end Engineers

Python Crash Course
Weeks 1 & 2
Hands-on with Generative AI
Weeks 3 & 4
Understanding Gen AI Architecture
Weeks 5 & 6
Building Applications & Use Cases
Weeks 7-10
Gen AI for Software Engineers Specialization & Capstone Projects
Weeks 11-14
Intelligent Virtual Assistant for Developers
Intelligent Meeting Summarizer
Data Structure & Algorithms
Weeks 15-19
Scalable System Design
Weeks 20-22
JavaScript Language & Libraries, UI & DOM
Weeks 23-24
Front-End System Design
Week 25
Advanced JavaScript and CSS
Week 26
Career Sessions: Mock Interviews, Feedback Sessions
Weeks 27-29
Support Period
Week 30 onward

Full-stack Engineers

Python Crash Course
Weeks 1 & 2
Hands-on with Generative AI
Weeks 3 & 4
Understanding Gen AI Architecture
Weeks 5 & 6
Building Applications & Use Cases
Weeks 7-10
Gen AI for Software Engineers Specialization & Capstone Projects
Weeks 11-14
Intelligent Virtual Assistant for Developers
Intelligent Meeting Summarizer
Data Structure & Algorithms
Weeks 15-19
Scalable System Design
Weeks 20-22
Database, API Design and Implementation
Weeks 23-24
Cloud Infrastructure, JavaScript and Web Development
Weeks 25-26
UI System Design
Week 27
Career Sessions: Mock Interviews, Feedback Sessions
Weeks 28-30
Support Period
Week 31 onward

Test Engineers

Python Crash Course
Weeks 1 & 2
Hands-on with Generative AI
Weeks 3 & 4
Understanding Gen AI Architecture
Weeks 5 & 6
Building Applications & Use Cases
Weeks 7-10
Gen AI for Software Engineers Specialization & Capstone Projects
Weeks 11-14
Intelligent Virtual Assistant for Developers
Intelligent Meeting Summarizer
Data Structure & Algorithms
Weeks 15-19
Scalable System Design
Weeks 20-22
Quality Engineering Foundations, Performance Testing
Weeks 23-24
API Testing, Automation Testing
Weeks 25-26
Test Automation Design Patterns, Cloud Testing
Week 27
Career Sessions: Mock Interviews, Feedback Sessions
Weeks 28-30
Support Period
Week 31 onward

Product Managers

Fundamentals of Python I
Week 1
Evolution of AI
Week 2
Fundamentals of Python II
Week 3
Hands-on with AI Models
Week 4
Deep Dive into AI for Text Generation
Week 5
Building AI Applications with Language Models
Week 6
Training AI Models for Text Generation
Week 7
Guided Live Project 1- Deploying AI Models with Streamlit
Week 8
Guided Live Project 2 - Building AI Financial Bot
Week 9
AI For Image Generation
Week 10
Guided Live Project -3 - Linkedin Headshot App
Week 11
GenAI for Audio/Guided Live Project 4 - Audio Synthesis with AI
Week 12
Capstone Projects - Product Management Focus
Week 13 onward
Personalized Sessions for Product Managers
Prompt Engineering and No Code Tools
Product Design and User Experience with AI & ML Products
Defining AI-Powered Product Requirements
AI Product Execution and Implementation
AI Product Strategies, Roadmaps & Execution
System Design for PMs
Weeks 15-16
Product Sense: Planning, Design, Estimation, Strategy
Week 17
Product Execution: Pricing, Design, Improvement and Growth
Week 18
Technical: Analytics, Metrics, Technical Concepts, Process Analysis
Weeks 19-22
Career Sessions: Mock Interviews, Feedback Sessions
Weeks 23-25
Support Period
Week 26 onward

Engineering Managers

Fundamentals of Python I
Week 1
Evolution of AI
Week 2
Fundamentals of Python II
Week 3
Hands-on with AI Models
Week 4
Deep Dive into AI for Text Generation
Week 5
Building AI Applications with Language Models
Week 6
Training AI Models for Text Generation
Week 7
Guided Live Project 1- Deploying AI Models with Streamlit
Week 8
Guided Live Project 2 - Building AI Financial Bot
Week 9
AI For Image Generation
Week 10
Guided Live Project -3 - Linkedin Headshot App
Week 11
GenAI for Audio/Guided Live Project 4 - Audio Synthesis with AI
Week 12
Capstone Projects - EM Focus
Week 13 onward
Personalized Sessions for Engineering Managers
Technical Feasibility and ROI of GenAI Projects
Getting ready for AI Solutions
Data Quality and Integration Challenges
Leading AI Teams and Providing Technical Guidance
AI-Driven Engineering System Enhancement
Scalable System Design
Weeks 15-17
Career & Leadership workshops
Weeks 18-21
Coding
Weeks 22-26
Technical Domain Course (DE/ML/DS/ES/FE/BE/FSE/SRE/Cloud/Android/iOS/Security)
Weeks 27-31
Career Sessions: Mock Interviews, Feedback Sessions
Weeks 32-34
Support Period
Week 35 onward

Technical Program Managers

Fundamentals of Python I
Week 1
Evolution of AI
Week 2
Fundamentals of Python II
Week 3
Hands-on with AI Models
Week 4
Deep Dive into AI for Text Generation
Week 5
Building AI Applications with Language Models
Week 6
Training AI Models for Text Generation
Week 7
Guided Live Project 1- Deploying AI Models with Streamlit
Week 8
Guided Live Project 2 - Building AI Financial Bot
Week 9
AI For Image Generation
Week 10
Guided Live Project -3 - Linkedin Headshot App
Week 11
GenAI for Audio/Guided Live Project 4 - Audio Synthesis with AI
Week 12
Capstone Projects - TPM Focus
Week 13 onward
Personalized Sessions for Technical Program Managers
Prompt Engineering and No Code Tools
Product Design with AI & ML blocks + User Experiences with AI
Defining AI-Powered Product Requirements
AI Product Execution and Implementation
Gen AI Project Lifecycle—Strategy to Execution
Scalable System Design
Weeks 15-16
Program Planning, Execution, Monitoring & Reporting
Weeks 17-19
Behavioral — Introducing Frameworks, Cross-Functional Cooperation, Motivation and Core Values
Weeks 20-22
Technical Domain Course (DE/ML/DS/ES/FE/BE/FSE/SRE/Cloud/Android/iOS/Security)
Weeks 23-27
Career Sessions: Mock Interviews, Feedback Sessions
Weeks 28-30
Support Period
Week 31 onward

Machine Learning Engineers

Deep Learning Primer, Neural Architectures, Gen AI: Background
Weeks 1-3
Deep-Dive into LLMs, LLMs in Production
Weeks 4-5
Diffusion Models, Multimodal Models
Weeks 6-7
Reinforcement Learning from Human Feedback
Weeks 8-9
Capstone Project
Week 10
AI-Powered Resume Coach
AI-Powered Conversational Shopping Assistant
Data Structure & Algorithms
Weeks 11-15
Scalable System Design
Weeks 16-18
Supervised Learning - Rank Relevant Search Results, Design a YouTube Video Recommendation System
Weeks 19-20
Unsupervised Learning - Detect Fraud Transactions for Airbnb
Week 21
Deep Learning - Detect and Process Objects in a Scene, Build a Tech Support Chatbot
Weeks 22-23
Career Sessions: Mock Interviews, Feedback Sessions
Weeks 24-26
Support Period
Week 27 onward

Data Scientists

Deep Learning Primer, Neural Architectures, Gen AI: Background
Weeks 1-3
Deep-Dive into LLMs, LLMs in Production
Weeks 4-5
Diffusion Models, Multimodal Models
Weeks 6-7
Reinforcement Learning from Human Feedback
Weeks 8-9
Capstone Project
Week 10
AI-Powered Resume Coach
AI-Powered Conversational Shopping Assistant
Data Structure and Algorithms
Weeks 11-15
SQL Programming
Week 16
Probability, Distributions
Weeks 17-18
Data Science Design: A/B testing, Regression, MLE, EM, and MAP
Weeks 19-20
Supervised & Unsupervised Machine Learning
Weeks 21-22
Deep Learning, Time Series Analysis
Weeks 23-24
Career Sessions: Mock Interviews, Feedback Sessions
Weeks 24-26
Support Period
Week 27 onward

Other Tech Professionals

Python Crash Course
Weeks 1 & 2
Hands-on with Generative AI
Weeks 3 & 4
Understanding Gen AI Architecture
Weeks 5 & 6
Building Applications & Use Cases
Weeks 7-10
Capstone Projects
Weeks 11-13
AI Customer Service Agent
AI Personal Shopper

Capstone Projects

General Tech Projects

AI Customer Service Agent

  • Design an LLM-based customer service agent capable of understanding and responding to customer queries in natural language, which can handle real-time customer interactions

AI Personal Shoppe

  • Create a personalized shopping assistant application using OpenAI's GPT-3.5 or a similar LLM, which can engage with customers through natural language, understanding their preferences, budget, and needs to recommend products they'll love.

Software Engineering Projects

Intelligent Virtual Assistant for Developers

  • Develop a virtual assistant for software developers that can understand complex programming queries, offer coding advice and debug tips, and even write small code snippets using LLM APIs.

Intelligent Meeting Summarizer

  •  Develop a tool that leverages LLMs to provide summaries and action items from virtual meetings. This app can transcribe conversations, highlight key points, and list tasks, saving time and ensuring that important details are captured and actioned upon.

Product Management Projects

AI Product Strategies, Roadmaps & Execution

  • Recommend user experience improvements and suggest new product features that leverage Generative AI, and create PRD/Product Strategies to integrate AI into existing products.

    Technical Program Management Projects

    Gen AI Project Lifecycle—Strategy to Execution

    • Develop a case study on the business integration of GenAI, detail the project management strategies used, analyze AI integration within existing systems, and include insights on ethical practices and regulatory compliance.

      Engineering Management Projects

      AI-Driven Engineering System Enhancement

      • Redesign an existing engineering system to seamlessly integrate Generative AI, emphasizing the enhancement of engineering efforts through automated decision-making, and evaluate tech stack modifications and scalability.

        General Tech Projects

        AI-Powered Resume Coach

        • Develop a Resume Coach that will analyze a user's resume and compare it to job descriptions and industry standards, providing constructive and personalized feedback and suggestions based on successful resumes in the field.

        Conversational Shopping Assistant

        • Create an AI assistant that understands detailed requests, and is able to provide precise product suggestions in real-time user interactions, and is able to respond in natural language.
        Capstone Projects are subject to change as per industry inputs.

          + Instructors to Train You in Live Classes

        Shruti Goli

        Senior Product Manager, Incode

        Ahsan Ali

        Applied Scientist

        Randy Cogill

        Senior Research Scientist

        Jay Pillai

        Engineering Leader - GenAI
        FAANG+ Leader

        The IK Experience: What Our Alumni Are Saying

        Our engineers land high-paying and rewarding offers from the biggest tech companies, including Facebook, Google, Microsoft, Apple, Amazon, Tesla, and Netflix.

        Mike Kane

        Lead Data Engineer
        Placed at :

        For many working professionals, going through examples and different perspectives are very valuable…. Interview Kickstart was great because its structure helped me understand each problem in my interview. The high sense of comradery in Discord was also great! I had a study group with other people in my cohort and felt the engagement was much stronger than in an academic setting.

        Davide Testuggine

        Software Engineer
        Placed at :

        What turned me to Soham’s course is the way he talked about the course as not a substitute for hard work, not a “cheat sheet” of questions but a way to actually get good at algorithms, through a lot of perspiration. The course is very intense…practice, practice, practice. And it works!.... All that practice had a long-lasting effect on my ability as a software engineer. I am simply faster at coding than I ever was…. I can keep focused on the idea if the implementation takes a few minutes as I don't get lost on implementation details anymore, so the productivity increase I experienced is greater than just the delta in time for the implementation itself.

        Mike Kane

        Lead Data Engineer
        Placed at :

        For many working professionals, going through examples and different perspectives are very valuable…. Interview Kickstart was great because its structure helped me understand each problem in my interview. The high sense of comradery in Discord was also great! I had a study group with other people in my cohort and felt the engagement was much stronger than in an academic setting.

        Davide Testuggine

        Software Engineer
        Placed at :

        What turned me to Soham’s course is the way he talked about the course as not a substitute for hard work, not a “cheat sheet” of questions but a way to actually get good at algorithms, through a lot of perspiration. The course is very intense…practice, practice, practice. And it works!.... All that practice had a long-lasting effect on my ability as a software engineer. I am simply faster at coding than I ever was…. I can keep focused on the idea if the implementation takes a few minutes as I don't get lost on implementation details anymore, so the productivity increase I experienced is greater than just the delta in time for the implementation itself.

        Get upto 15 mock interviews with   hiring manager

        What makes our mock Interviews the best:

        Hiring Managers from Tier-1 companies like Google & Apple

        Interview with the best. No one will prepare you better!

        Transparent, non-anonymous interviews

        Get the most realistic experience possible

        Domain-specific Interviews

        Practice for your target domain - Back-End Engineering

        Detailed personalized feedback

        Identify and work on your improvement areas

        How to Enroll for Interview Kickstart’s EdgeUp Program

        Learn more about Interview Kickstart and the EdgeUp Program by joining the free pre-enrollment webinar.

        Register for webinar
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