Job Description
This is a remote position.
Module 1 : Introduction to AI and Generative AI
- 1.1 Fundamentals of AI : ○ Definition and scope of AI ○ Types of AI (e.g., supervised, unsupervised, reinforcement learning) 1
Key concepts in machine learning (e.g., training, testing, evaluation)
1.2 Introduction to Generative AI : ○ Definition and capabilities of Generative AI ○ Differentiating Generative AI from traditional AI ○ Core concepts in Generative AI (e.g., large language models, generative adversarial networks) ○ Specific generative models like GANs (Generative Adversarial Networks) and VAEs 2 ○ Ethical considerations in Generative AI development and deployment 1 ○ Example Learning Resource : The "Generative AI for Beginners" course by Microsoft offers a comprehensive introduction to Generative AI concepts and practical applications. This course features a structured learning path with "Learn" and "Build" lessons covering various topics, including prompt engineering, image generation, and low-code AI application development. It can serve as a valuable starting point for participants seeking a foundational understanding of Generative AI3.Module 2 : AI / Generative AI for Product Enhancement
2.1 Use Cases and Examples : ○ Enhancing user experience with AI-powered personalization ○ Automating tasks and workflows with AI ○ Improving product design and development with Generative AI ○ Creating new product features and functionalities with AI / Generative AI ○ AI-driven testing : AI can be used to automate and enhance software testing processes, leading to increased test coverage, efficiency, and reliability4. ○ Code optimization and rapid experimentation : Large Language Models (LLMs) can assist in optimizing code quality, identifying bugs, and facilitating rapid experimentation with new features5. ○ Personalized user experiences : AI can be utilized to create applications that adapt to individual user preferences, leading to increased user engagement and satisfaction6. ○ Generative AI in education : Students are already leveraging Generative AI for various learning purposes, such as summarizing text, organizing schedules, and understanding complex concepts. This highlights the practical applications of Generative AI and its growing relevance in education, which can inspire software engineers to explore similar applications in product development7. ○ Examples of successful AI / Generative AI implementations in various industries (to be added)2.2 Identifying Opportunities : ○ Analyzing existing products to identify areas for AI-driven enhancement ○ Brainstorming new product ideas leveraging AI / Generative AI capabilities ○ Evaluating the feasibility and potential impact of AI solutions2.3 Ethical Considerations : ○ The GENAI Assessment Scale provides a framework for defining and communicating acceptable uses of Generative AI in different contexts. Developers can use this scale to consider the ethical implications of their AI-powered solutions and ensure responsible development8.Module 3 : Tools and Technologies
3.1 Popular AI / Generative AI Frameworks : ○ Overview of TensorFlow, PyTorch, and other relevant frameworks ○ Choosing the right framework for specific product enhancement tasksFramework Description Key Features Use Cases
TensorFlow Open-source machine learning framework developed by Google
Scalability, flexibility, support for various AI tasks
Image recognition, natural language processing, deep learning
PyTorch Open-source machine learning framework developed by Meta
Dynamic computation graphs, ease of use, strong community support
Natural language processing, computer vision, research
Add other frameworks here)
3.2 Generative AI Tools and Platforms : ○ Introduction to cloud-based AI platforms (e.g., Azure OpenAI, AWS Bedrock) ○ Amazon Bedrock : Amazon Bedrock is a fully managed service that offers a variety of foundation models (FMs) and tools for building and scaling generative AI applications. It provides access to leading FMs and includes resources like the "Building Generative AI Applications using Amazon Bedrock" course, which focuses on practical application development9. ○ Exploring open-source Generative AI tools and libraries3.3 Working with APIs and SDKs : ○ Integrating AI / Generative AI models into applications using APIs and SDKs ○ Best practices for API management and securityRequirements
Module 1 : Introduction to AI and Generative AI
1.1 Fundamentals of AI : ○ Definition and scope of AI ○ Types of AI (e.g., supervised, unsupervised, reinforcement learning) 1 ○ Key concepts in machine learning (e.g., training, testing, evaluation)1.2 Introduction to Generative AI : ○ Definition and capabilities of Generative AI ○ Differentiating Generative AI from traditional AI ○ Core concepts in Generative AI (e.g., large language models, generative adversarial networks) ○ Specific generative models like GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders) 2 ○ Ethical considerations in Generative AI development and deployment 1 ○ Example Learning Resource : The "Generative AI for Beginners" course by Microsoft offers a comprehensive introduction to Generative AI concepts and practical applications. This course features a structured learning path with "Learn" and "Build" lessons covering various topics, including prompt engineering, image generation, and low-code AI application development. It can serve as a valuable starting point for participants seeking a foundational understanding of Generative AI3. Module 2 : AI / Generative AI for Product Enhancement2.1 Use Cases and Examples : ○ Enhancing user experience with AI-powered personalization ○ Automating tasks and workflows with AI ○ Improving product design and development with Generative AI ○ Creating new product features and functionalities with AI / Generative AI ○ AI-driven testing : AI can be used to automate and enhance software testing processes, leading to increased test coverage, efficiency, and reliability4. ○ Code optimization and rapid experimentation : Large Language Models (LLMs) can assist in optimizing code quality, identifying bugs, and facilitating rapid experimentation with new features5. ○ Personalized user experiences : AI can be utilized to create applications that adapt to individual user preferences, leading to increased user engagement and satisfaction6. ○ Generative AI in education : Students are already leveraging Generative AI for various learning purposes, such as summarizing text, organizing schedules, and understanding complex concepts. This highlights the practical applications of Generative AI and its growing relevance in education, which can inspire software engineers to explore similar applications in product development7. ○ Examples of successful AI / Generative AI implementations in various industries (to be added)2.2 Identifying Opportunities : ○ Analyzing existing products to identify areas for AI-driven enhancement ○ Brainstorming new product ideas leveraging AI / Generative AI capabilities ○ Evaluating the feasibility and potential impact of AI solutions2.3 Ethical Considerations : ○ The GENAI Assessment Scale provides a framework for defining and communicating acceptable uses of Generative AI in different contexts. Developers can use this scale to consider the ethical implications of their AI-powered solutions and ensure responsible development8. Module 3 : Tools and Technologies3.1 Popular AI / Generative AI Frameworks : ○ Overview of TensorFlow, PyTorch, and other relevant frameworks ○ Choosing the right framework for specific product enhancement tasks Framework Description Key Features Use Cases TensorFlow Open-source machine learning framework developed by Google Scalability, flexibility, support for various AI tasks Image recognition, natural language processing, deep learning PyTorch Open-source machine learning framework developed by Meta Dynamic computation graphs, ease of use, strong community support Natural language processing, computer vision, research (Add other frameworks here)3.2 Generative AI Tools and Platforms : ○ Introduction to cloud-based AI platforms (e.g., Azure OpenAI, AWS Bedrock) ○ Amazon Bedrock : Amazon Bedrock is a fully managed service that offers a variety of foundation models (FMs) and tools for building and scaling generative AI applications. It provides access to leading FMs and includes resources like the "Building Generative AI Applications using Amazon Bedrock" course, which focuses on practical application development9. ○ Exploring open-source Generative AI tools and libraries3.3 Working with APIs and SDKs : ○ Integrating AI / Generative AI models into applications using APIs and SDKs ○ Best practices for API management and security Module 4 : Hands-on Labs This module will provide participants with opportunities to apply the concepts learned in previous
