Advanced Generative AI Engineer (Job Guarantee) Program

Course Prerequisite(s)

Please note that this course has the following prerequisites which must be completed before it can be accessed
Professional Agentic AI Engineer Program

About Course

Build, fine-tune, and deploy enterprise-scale Generative AI systems using LLMs, MLOps, and cloud technologies.

The Generative AI Engineer Advanced Program is designed to develop highly skilled professionals capable of building, optimizing, and deploying advanced generative AI systems at scale. This program covers cutting-edge topics such as large language model fine-tuning, diffusion models, multimodal AI, and production-grade MLOps, enabling learners to work on real-world AI systems across industries.

Learners will gain hands-on experience with advanced architectures, scalable data pipelines, and cloud-native deployment strategies. The program emphasizes performance optimization, automation, and reliability, ensuring learners understand how to transition from experimental models to enterprise-level AI systems.

By the end of this program, learners will be equipped to design, build, and deploy full-scale generative AI solutions, making them ready for high-impact roles in AI engineering and applied AI domains.

Build and optimize advanced generative AI systems
Fine-tune large language models for real-world applications
Develop multimodal AI systems combining text, images, and other data
Work with advanced generative models such as GANs, VAEs, and diffusion models
Design scalable AI pipelines and workflows
Implement MLOps practices for deployment, monitoring, and scaling
Use Docker and Kubernetes for containerized AI systems
Apply reinforcement learning in generative AI systems
Build AI applications for domains such as healthcare, finance, and media
Ensure ethical AI practices, security, and bias mitigation
Deliver end-to-end capstone projects with industry mentorship

Course Content

Module 1: Foundations of Generative AI
This module reinforces core concepts of generative AI, ensuring learners have a strong base before moving into advanced topics. It revisits key principles and prepares learners for complex architectures and real-world applications.

Module 2: Advanced Mathematics for Generative AI
This module deepens understanding of mathematical concepts required for advanced generative models. Learners will explore optimization techniques and mathematical foundations that improve model performance and stability.

Module 3: Advanced Programming for Generative AI Systems
This module focuses on writing efficient and scalable code for generative AI systems. Learners will understand best practices for structuring AI applications and optimizing performance.

Module 4: Deep Learning Architecture Optimization
This module focuses on optimizing neural network architectures for better performance and scalability. Learners will explore techniques to improve training efficiency and model accuracy.

Module 5: Advanced Generative Models
This module explores advanced implementations of GANs and VAEs, focusing on improving model quality and stability. Learners will work on real-world use cases and understand practical challenges.

Module 6: Large Language Models & Fine-tuning Techniques
This module focuses on working with large language models and fine-tuning them for specific tasks. Learners will understand how to adapt pre-trained models to real-world applications.

Module 7: Diffusion Models & Advanced Multimodal Generation
This module introduces advanced diffusion models and multimodal AI systems that combine text, images, and other data types. Learners will explore cutting-edge techniques used in modern AI applications.

Module 8: Ethical AI & Responsible AI Practices
This module focuses on building ethical and responsible AI systems. Learners will understand bias detection, fairness, and governance practices required in real-world AI deployments.

Module 9: AI Deployment & MLOps for Generative AI
This module provides in-depth knowledge of deploying generative AI systems using MLOps practices. Learners will understand model lifecycle management, monitoring, and scaling.

Module 10: Cloud Platforms & Containerization
This module introduces cloud platforms and containerization tools such as Docker and Kubernetes. Learners will understand how to deploy and manage AI systems in scalable environments.

Module 11: Advanced Data Engineering & Pipeline Management
This module focuses on building efficient data pipelines required for generative AI systems. Learners will understand data flow, transformation, and management at scale.

Module 12: Reinforcement Learning with Generative Models
This module introduces reinforcement learning techniques applied to generative AI systems. Learners will explore how models improve through feedback and interaction.

Module 13: AI for Automated Code Generation & Synthesis
This module focuses on building AI systems that can generate code and automate development tasks. Learners will explore real-world applications of AI in software engineering.

Module 14: Industry-specific Generative AI Applications
This module explores how generative AI is applied across industries such as healthcare, finance, and media. Learners will understand domain-specific use cases and implementation strategies.

Module 15: AI Security & Bias Mitigation Strategies
This module focuses on securing AI systems and mitigating risks such as bias and misuse. Learners will understand best practices for building safe and reliable AI solutions.

Module 16: Capstone Project – Real-world Generative AI Solution
This module is a comprehensive project where learners build and deploy a real-world generative AI system. It involves end-to-end implementation, including data processing, model development, and deployment.

Module 17: Internship & Industry Mentoring
This module provides real-world exposure through mentorship or internship opportunities. Learners will work on practical assignments and gain insights into industry workflows and expectations.

Module 18: Job Readiness & Career Acceleration
This module includes all components from the professional program, focusing on resume building, portfolio development, LinkedIn optimization, and interview preparation to ensure career readiness.

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A course by

Gangula Vishwas

Data analyst/ML Engineer

Advanced Generative AI Engineer (Job Guarantee) Program

Course Prerequisite(s)

About Course

Build, fine-tune, and deploy enterprise-scale Generative AI systems using LLMs, MLOps, and cloud technologies.

What Will You Learn?

Course Content

Module 1: Foundations of Generative AI
This module reinforces core concepts of generative AI, ensuring learners have a strong base before moving into advanced topics. It revisits key principles and prepares learners for complex architectures and real-world applications.

Module 2: Advanced Mathematics for Generative AI
This module deepens understanding of mathematical concepts required for advanced generative models. Learners will explore optimization techniques and mathematical foundations that improve model performance and stability.

Module 3: Advanced Programming for Generative AI Systems
This module focuses on writing efficient and scalable code for generative AI systems. Learners will understand best practices for structuring AI applications and optimizing performance.

Module 4: Deep Learning Architecture Optimization
This module focuses on optimizing neural network architectures for better performance and scalability. Learners will explore techniques to improve training efficiency and model accuracy.

Module 5: Advanced Generative Models
This module explores advanced implementations of GANs and VAEs, focusing on improving model quality and stability. Learners will work on real-world use cases and understand practical challenges.

Module 6: Large Language Models & Fine-tuning Techniques
This module focuses on working with large language models and fine-tuning them for specific tasks. Learners will understand how to adapt pre-trained models to real-world applications.

Module 7: Diffusion Models & Advanced Multimodal Generation
This module introduces advanced diffusion models and multimodal AI systems that combine text, images, and other data types. Learners will explore cutting-edge techniques used in modern AI applications.

Module 8: Ethical AI & Responsible AI Practices
This module focuses on building ethical and responsible AI systems. Learners will understand bias detection, fairness, and governance practices required in real-world AI deployments.

Module 9: AI Deployment & MLOps for Generative AI
This module provides in-depth knowledge of deploying generative AI systems using MLOps practices. Learners will understand model lifecycle management, monitoring, and scaling.

Module 10: Cloud Platforms & Containerization
This module introduces cloud platforms and containerization tools such as Docker and Kubernetes. Learners will understand how to deploy and manage AI systems in scalable environments.

Module 11: Advanced Data Engineering & Pipeline Management
This module focuses on building efficient data pipelines required for generative AI systems. Learners will understand data flow, transformation, and management at scale.

Module 12: Reinforcement Learning with Generative Models
This module introduces reinforcement learning techniques applied to generative AI systems. Learners will explore how models improve through feedback and interaction.

Module 13: AI for Automated Code Generation & Synthesis
This module focuses on building AI systems that can generate code and automate development tasks. Learners will explore real-world applications of AI in software engineering.

Module 14: Industry-specific Generative AI Applications
This module explores how generative AI is applied across industries such as healthcare, finance, and media. Learners will understand domain-specific use cases and implementation strategies.

Module 15: AI Security & Bias Mitigation Strategies
This module focuses on securing AI systems and mitigating risks such as bias and misuse. Learners will understand best practices for building safe and reliable AI solutions.

Module 16: Capstone Project – Real-world Generative AI Solution
This module is a comprehensive project where learners build and deploy a real-world generative AI system. It involves end-to-end implementation, including data processing, model development, and deployment.

Module 17: Internship & Industry Mentoring
This module provides real-world exposure through mentorship or internship opportunities. Learners will work on practical assignments and gain insights into industry workflows and expectations.

Module 18: Job Readiness & Career Acceleration
This module includes all components from the professional program, focusing on resume building, portfolio development, LinkedIn optimization, and interview preparation to ensure career readiness.

Earn a certificate

About Learning

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Popular Subjects

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Course Prerequisite(s)

About Course

Build, fine-tune, and deploy enterprise-scale Generative AI systems using LLMs, MLOps, and cloud technologies.

What Will You Learn?

Course Content

Module 1: Foundations of Generative AI This module reinforces core concepts of generative AI, ensuring learners have a strong base before moving into advanced topics. It revisits key principles and prepares learners for complex architectures and real-world applications.

Module 2: Advanced Mathematics for Generative AI This module deepens understanding of mathematical concepts required for advanced generative models. Learners will explore optimization techniques and mathematical foundations that improve model performance and stability.

Module 3: Advanced Programming for Generative AI Systems This module focuses on writing efficient and scalable code for generative AI systems. Learners will understand best practices for structuring AI applications and optimizing performance.

Module 4: Deep Learning Architecture Optimization This module focuses on optimizing neural network architectures for better performance and scalability. Learners will explore techniques to improve training efficiency and model accuracy.

Module 5: Advanced Generative Models This module explores advanced implementations of GANs and VAEs, focusing on improving model quality and stability. Learners will work on real-world use cases and understand practical challenges.

Module 6: Large Language Models & Fine-tuning Techniques This module focuses on working with large language models and fine-tuning them for specific tasks. Learners will understand how to adapt pre-trained models to real-world applications.

Module 7: Diffusion Models & Advanced Multimodal Generation This module introduces advanced diffusion models and multimodal AI systems that combine text, images, and other data types. Learners will explore cutting-edge techniques used in modern AI applications.

Module 8: Ethical AI & Responsible AI Practices This module focuses on building ethical and responsible AI systems. Learners will understand bias detection, fairness, and governance practices required in real-world AI deployments.

Module 9: AI Deployment & MLOps for Generative AI This module provides in-depth knowledge of deploying generative AI systems using MLOps practices. Learners will understand model lifecycle management, monitoring, and scaling.

Module 10: Cloud Platforms & Containerization This module introduces cloud platforms and containerization tools such as Docker and Kubernetes. Learners will understand how to deploy and manage AI systems in scalable environments.

Module 11: Advanced Data Engineering & Pipeline Management This module focuses on building efficient data pipelines required for generative AI systems. Learners will understand data flow, transformation, and management at scale.

Module 12: Reinforcement Learning with Generative Models This module introduces reinforcement learning techniques applied to generative AI systems. Learners will explore how models improve through feedback and interaction.

Module 13: AI for Automated Code Generation & Synthesis This module focuses on building AI systems that can generate code and automate development tasks. Learners will explore real-world applications of AI in software engineering.

Module 14: Industry-specific Generative AI Applications This module explores how generative AI is applied across industries such as healthcare, finance, and media. Learners will understand domain-specific use cases and implementation strategies.

Module 15: AI Security & Bias Mitigation Strategies This module focuses on securing AI systems and mitigating risks such as bias and misuse. Learners will understand best practices for building safe and reliable AI solutions.

Module 16: Capstone Project – Real-world Generative AI Solution This module is a comprehensive project where learners build and deploy a real-world generative AI system. It involves end-to-end implementation, including data processing, model development, and deployment.

Module 17: Internship & Industry Mentoring This module provides real-world exposure through mentorship or internship opportunities. Learners will work on practical assignments and gain insights into industry workflows and expectations.

Module 18: Job Readiness & Career Acceleration This module includes all components from the professional program, focusing on resume building, portfolio development, LinkedIn optimization, and interview preparation to ensure career readiness.

Earn a certificate

Student Ratings & Reviews

About Learning

Other links

Popular Subjects

Get In Touch

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Module 1: Foundations of Generative AI
This module reinforces core concepts of generative AI, ensuring learners have a strong base before moving into advanced topics. It revisits key principles and prepares learners for complex architectures and real-world applications.

Module 2: Advanced Mathematics for Generative AI
This module deepens understanding of mathematical concepts required for advanced generative models. Learners will explore optimization techniques and mathematical foundations that improve model performance and stability.

Module 3: Advanced Programming for Generative AI Systems
This module focuses on writing efficient and scalable code for generative AI systems. Learners will understand best practices for structuring AI applications and optimizing performance.

Module 4: Deep Learning Architecture Optimization
This module focuses on optimizing neural network architectures for better performance and scalability. Learners will explore techniques to improve training efficiency and model accuracy.

Module 5: Advanced Generative Models
This module explores advanced implementations of GANs and VAEs, focusing on improving model quality and stability. Learners will work on real-world use cases and understand practical challenges.

Module 6: Large Language Models & Fine-tuning Techniques
This module focuses on working with large language models and fine-tuning them for specific tasks. Learners will understand how to adapt pre-trained models to real-world applications.

Module 7: Diffusion Models & Advanced Multimodal Generation
This module introduces advanced diffusion models and multimodal AI systems that combine text, images, and other data types. Learners will explore cutting-edge techniques used in modern AI applications.

Module 8: Ethical AI & Responsible AI Practices
This module focuses on building ethical and responsible AI systems. Learners will understand bias detection, fairness, and governance practices required in real-world AI deployments.

Module 9: AI Deployment & MLOps for Generative AI
This module provides in-depth knowledge of deploying generative AI systems using MLOps practices. Learners will understand model lifecycle management, monitoring, and scaling.

Module 10: Cloud Platforms & Containerization
This module introduces cloud platforms and containerization tools such as Docker and Kubernetes. Learners will understand how to deploy and manage AI systems in scalable environments.

Module 11: Advanced Data Engineering & Pipeline Management
This module focuses on building efficient data pipelines required for generative AI systems. Learners will understand data flow, transformation, and management at scale.

Module 12: Reinforcement Learning with Generative Models
This module introduces reinforcement learning techniques applied to generative AI systems. Learners will explore how models improve through feedback and interaction.

Module 13: AI for Automated Code Generation & Synthesis
This module focuses on building AI systems that can generate code and automate development tasks. Learners will explore real-world applications of AI in software engineering.

Module 14: Industry-specific Generative AI Applications
This module explores how generative AI is applied across industries such as healthcare, finance, and media. Learners will understand domain-specific use cases and implementation strategies.

Module 15: AI Security & Bias Mitigation Strategies
This module focuses on securing AI systems and mitigating risks such as bias and misuse. Learners will understand best practices for building safe and reliable AI solutions.

Module 16: Capstone Project – Real-world Generative AI Solution
This module is a comprehensive project where learners build and deploy a real-world generative AI system. It involves end-to-end implementation, including data processing, model development, and deployment.

Module 17: Internship & Industry Mentoring
This module provides real-world exposure through mentorship or internship opportunities. Learners will work on practical assignments and gain insights into industry workflows and expectations.

Module 18: Job Readiness & Career Acceleration
This module includes all components from the professional program, focusing on resume building, portfolio development, LinkedIn optimization, and interview preparation to ensure career readiness.