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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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Instructors, Coaches & Interviewers from Top Companies
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Tools you’ll learn

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

Back-end Engineers

4.5

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Front-end Engineers

4.6

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Full-stack Engineers

4.7

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Test Engineers

4.8

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Product Managers

4.8

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Engineering Managers

4.7

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Technical Program Managers

4.7

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Machine Learning Engineers

4.6

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Data Scientists

4.6

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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-Driven Engineering System Enhancement
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 35 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.

falag + Instructors to Train You in Live Classes

Randy Cogill

Senior Research Scientist
instructor

Jay Pillai

Engineering Leader - GenAI

FAANG+ Leader

Shruti Goli

Senior Product Manager, Incode

Ahsan Ali

Applied Scientist
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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

Senior Software Engineer

Placed at:

My experience at IK was extremely positive. I was preparing for FAANG companies using the standard techniques that you find on the internet. When I started preparing, there was no structure to the madness. For example, a simple quicksort can be implemented in multiple ways. So solving a medium problem would take me about 30 minutes. The biggest benefit that I got from IK was a clear, structured way of solving problems. After IK, I could solve medium problems in 10 minutes!

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 falag 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!

Domain-specific Interviews

Practice for your target domain - Back-End Engineering

Detailed personalized feedback

Identify and work on your improvement areas

Transparent, non-anonymous interviews

Get the most realistic experience possible

1. Flexible schedule

Pick timings convenient to you

4. Technical and behavioral interviews

Uplevel your technical and behavioral interview skills

2. Remote interview experience

Mirrors the current format of remote interviews

5. Level-specific interviews

Because an L4 at Google can be quite different from an E7 at Meta

3. Feedback documentation

All the feedback you’ve ever wanted, recorded and documented

6. Interviewer of your choice

Choose based on domain

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.

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FAQs

Interview Kickstart’s EdgeUp Program is a unique course designed to equip you with Generative AI skills relevant to your domain and job role AND prepare you for interviews at top tech companies. You will not only gain the skills required to stay relevant in today’s AI-drien techworld, but also be ready to ace interviews to land your dream job.
EdgeUp is a combination of Applied Generative AI and our industry-acclaimed interview prep programs. You will first spend around 14 weeks training on Generative AI and learning the practical applications of it in your domain, and spend another 12-16 weeks on interview preparation that incorporates both your current domain and Generative AI integration.
Yes. The first half of this program will be about building GenAI literacy and technological fluency for creative application in various professional roles. The second half is interview preparation, through which you will be prepared for the toughest interviews in the tech world today.
Many companies have started adding Generative AI skills to job descriptions of several tech roles including Software Engineering, Engineering Managers, Product Managers, and Technical Program Managers. We analyzed over 10,000 JDs and found that over 10% of the roles now require GenAI skills. You need to know not just how to use various GenAI tools such as GPT and Midjourney, but also how to best leverage the power of GenAI to increase productivity, efficiency, and creativity.
This program is suitable for anyone in the tech field with a strong inclination to learn Generative AI, and land their dream roles at top-tier tech companies.
The best way to join the course is to first register for our pre-enrolment session here. You will learn all about the course, its cost, and other useful details.
Applied GenAI modules are all live, and the domain-level classes could be either self-paced or live. You will also receive class recordings after each day’s session.
You will need to put in around 8 to 12 hours each week for this course.
Yes. You will be able to access all class recordings even after you complete the program.
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