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Generative AI Tools for Modern Software Engineering

Edureka

This program offers a structured journey into the transformative world of AI-powered code understanding, quality assurance, and intelligent development workflows. Designed for developers, software engineers, and technical leads, this course empowers you to leverage cutting-edge AI tools for efficient code navigation, review, debugging, security, and optimization.

By the end of this program, you will be able to:

- Analyze and explore large codebases quickly with AI tools for faster understanding and onboarding.

By the end of this program, you will be able to:

- Analyze and explore large codebases quickly with AI tools for faster understanding and onboarding.

- Review and evaluate code automatically to ensure high-quality, reliable, and maintainable software.

- Create, refactor, and debug code efficiently using intelligent AI-powered assistants.

- Secure applications by detecting vulnerabilities, managing dependencies, and enhancing code safety.

- Optimize and improve performance with AI-driven profiling, tuning, and resource management tools.

This program is ideal for software engineers, AI professionals, and tech leads aiming to enhance their coding workflows with AI. A foundational understanding of programming concepts, version control, and basic software development practices is recommended.

Join us to unlock the power of AI in software engineering and transform the way you navigate, build, and maintain code.

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What's inside

Syllabus

AI for Code Understanding and Quality

This module explores AI-powered tools for code navigation, understanding, and quality improvement. Learners gain hands-on experience with tools like Cursor AI, CodeSee, Sourcegraph, and Qodo to analyze codebases, perform reviews, detect issues early, and enhance software reliability.

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Activities

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Career center

Learners who complete Generative AI Tools for Modern Software Engineering will develop knowledge and skills that may be useful to these careers:

Software Engineer

A Software Engineer designs, develops, and maintains software applications and systems. This course directly enhances the core skills of a Software Engineer by empowering them to leverage cutting-edge AI tools for efficient code navigation, review, debugging, security, and optimization. Learners gain hands-on experience with AI-powered assistants for code creation, refactoring, and detecting vulnerabilities. By exploring AI tools for code understanding and quality, such as Cursor AI or Sourcegraph, individuals can analyze large codebases quickly, ensuring high-quality, reliable, and maintainable software. This course transforms daily workflows, leading to significantly improved productivity and software integrity.

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Full-Stack Developer

A Full Stack Developer works across all layers of an application, from front-end to back-end, handling diverse coding tasks. This course is exceptionally relevant for a Full Stack Developer, as it offers a structured journey into leveraging AI tools for every stage of development. Learners gain hands-on experience in analyzing codebases quickly, reviewing and evaluating code automatically, and creating, refactoring, and debugging code efficiently using intelligent AI-powered assistants. The program's coverage of securing applications and optimizing performance with AI-driven tools directly enhances the comprehensive skill set required to build and maintain robust, high-quality, and secure full-stack solutions.

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AI Tools Specialist

An AI Tools Specialist focuses on implementing, customizing, and managing artificial intelligence tools to improve organizational efficiency and workflows. This course provides the foundational and practical expertise required for an AI Tools Specialist to excel, offering a structured journey into the transformative world of AI-powered code understanding, quality assurance, and intelligent development workflows. Learners gain hands-on experience with a wide array of specific AI tools such as Cursor AI, Codeium, Snyk, and DeepSource across modules covering code understanding, creation, debugging, security, and optimization. This comprehensive exposure helps professionals strategically integrate and manage these advanced solutions.

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Technical Lead

A Technical Lead guides development teams, overseeing technical direction, code quality, and project execution. This course is specifically designed to empower a Technical Lead to leverage cutting-edge AI tools for efficient code navigation, review, debugging, security, and optimization across their team's projects. Learners will gain understanding in how AI can streamline workflows, analyze large codebases quickly for faster understanding, and review code automatically to ensure high standards. The program's emphasis on collaborative development with AI tools helps improve team efficiency and fosters a culture of high-quality, secure software delivery.

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Security Engineer

A Security Engineer designs, implements, and maintains robust security measures to protect software systems and data. This course is highly relevant for a Security Engineer, as it focuses on securing applications by detecting vulnerabilities, managing dependencies, and enhancing code safety through cutting-edge AI tools. Learners explore AI-driven secure development using tools like Snyk, DeepSource, and CodeAnt to identify and mitigate risks proactively. The program covers vulnerability detection and secure coding practices, which are essential for building resilient and trustworthy software. This specialized knowledge empowers professionals to integrate AI into their security strategies, strengthening defenses against evolving threats.

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DevOps Engineer

As a DevOps Engineer, you bridge development and operations, automating and optimizing the software delivery lifecycle. This course offers invaluable insights into integrating generative AI tools to enhance continuous integration, continuous delivery, and infrastructure as code practices. Learners explore AI-driven performance optimization, resource management, and workflow automation, which are central to a DevOps Engineer's role. Modules dedicated to AI for secure, optimized, and collaborative development, utilizing tools like Snyk and DeepSource, directly support automating vulnerability detection and ensuring highly performant systems. This program helps implement intelligent, efficient, and secure deployment pipelines.

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Site Reliability Engineer

A Site Reliability Engineer focuses on the reliability, performance, and operational efficiency of large-scale systems. This course offers valuable tools and techniques for a Site Reliability Engineer by exploring AI-driven profiling, tuning, and resource management to optimize and improve performance. Learners delve into AI for secure, optimized, and collaborative development, which is critical for maintaining robust and resilient systems. Topics such as vulnerability detection with tools like Snyk and performance optimization directly contribute to enhanced system stability and faster incident resolution. This program helps professionals implement proactive AI-powered strategies for operational excellence.

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Quality Assurance Engineer

A Quality Assurance Engineer is responsible for ensuring software quality, reliability, and performance. This course directly supports the objectives of a Quality Assurance Engineer by exploring AI-powered tools for automated code review, issue detection, and performance optimization. Learners gain practical experience with technologies like Codacy, DeepSource, mabl, and Katalon, which facilitate reviewing and evaluating code automatically to ensure high-quality, reliable, and maintainable software. The program's focus on detecting issues early, enhancing code safety, and AI-driven testing strategies is instrumental for refining QA processes and achieving superior product standards.

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Release Engineer

A Release Engineer manages and orchestrates the entire software release process, ensuring timely and reliable deployments. For a Release Engineer, this course provides critical insights into how generative AI tools can enhance the integrity and efficiency of releases. Learners explore AI-powered quality assurance, security, and optimization, which directly impact the readiness and stability of software. Modules covering vulnerability detection, performance optimization with AI-driven profiling, and automated code review using tools like DeepSource or Snyk are highly relevant. This program helps implement a data-driven approach to release management, ensuring high-quality, secure, and performant software reaches production efficiently.

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Machine Learning Engineer

A Machine Learning Engineer focuses on designing, building, and deploying scalable machine learning systems. While the course isn't about creating AI models, it profoundly impacts the software engineering aspects inherent in an Machine Learning Engineer's role. Learners will discover how to leverage generative AI tools for efficient code development, debugging, and review, which is critical when managing complex ML pipelines and models. The program's emphasis on AI-powered code creation, optimization, and securing applications assists in building robust and maintainable ML infrastructure. Understanding these AI tools helps streamline development workflows for ML applications.

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Research Engineer

A Research Engineer blends scientific research with practical engineering, often prototyping novel ideas and building experimental systems. This course offers a significant advantage for a Research Engineer by equipping them with generative AI tools for accelerated development and higher code quality in their experimental work. Learners explore AI-powered code creation, refactoring, and debugging with intelligent assistants, crucial for quickly iterating on research concepts. The program's focus on AI for secure, optimized, and collaborative development ensures that prototypes are not only innovative but also robust and maintainable. This helps streamline the transition from research concepts to tangible, high-quality implementations.

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Solutions Architect

A Solutions Architect designs and oversees the implementation of complex IT solutions, ensuring they meet business and technical requirements. For a Solutions Architect, understanding how generative AI tools can transform development processes is crucial for designing modern, efficient, and secure architectures. The course explores AI-powered code understanding, quality assurance, and intelligent development workflows, which influence architectural decisions related to maintainability, security, and performance. Insights into AI-driven profiling, vulnerability detection with tools like Snyk, and performance optimization enable architects to recommend and integrate best practices for highly robust and scalable systems.

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Software Development Manager

A Software Development Manager leads engineering teams, overseeing project delivery, resource allocation, and workflow efficiency. For a Software Development Manager, understanding generative AI tools for modern software engineering is crucial for optimizing team productivity and strategic decision-making. The course explores AI-powered code understanding, quality assurance, and intelligent development workflows, enabling managers to identify opportunities for process improvement. Insights into AI-driven secure development, performance optimization, and enhanced team collaboration using tools like CodeSee and Grit.io can lead to more efficient development cycles and higher quality deliverables. This program may be useful for fostering innovation and implementing cutting-edge practices.

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Technical Product Manager

A Technical Product Manager defines product strategy, roadmaps, and requirements, bridging technical capabilities with market needs. This course may be useful for a Technical Product Manager by providing a deep understanding of cutting-edge generative AI tools used in software development. Learners gain insight into AI-powered code creation, quality assurance, and security, which can inform decisions about developer-facing products or internal tooling. Understanding how AI can accelerate development, optimize performance, and enhance code safety allows product managers to better evaluate feasibility, define intelligent features, and articulate the value of advanced engineering practices to stakeholders, thereby driving product success.

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Data Engineer

A Data Engineer designs and builds systems for collecting, managing, and transforming data, often involving extensive coding. This course may be useful for a Data Engineer by providing robust techniques for handling code quality and efficiency. While the course focuses on general software engineering, the principles of AI-powered code understanding, creation, debugging, and optimization are highly transferable to data pipeline development. Learners will explore AI tools for analyzing large codebases quickly, refactoring code efficiently, and detecting issues early, which are invaluable skills when working with complex data infrastructure. The program supports building more reliable, maintainable, and secure data systems.

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