Yes. Based on your resume and the AI solutions you’ve described building, a strong interview answer would be:

Yes. I have experience using AI-assisted development tools, particularly GitHub Copilot, and I’ve evaluated tools like Cursor and Devin for improving developer productivity and accelerating software delivery.

You can expand on it like this:

GitHub Copilot

• Used GitHub Copilot within VS Code for Python, SQL, and infrastructure development.
• Generated boilerplate code for:
  • AWS Lambda functions
  • AWS Glue ETL jobs (PySpark/Python)
  • REST APIs
  • Unit tests
  • SQL queries
• Accelerated development of:
  • Boto3 integrations
  • Prompt engineering experiments
  • RAG pipelines
  • Cloud automation scripts
• Used Copilot to:
  • Explain unfamiliar code
  • Refactor legacy code
  • Generate documentation
  • Improve code quality

Example:

“While developing AWS Glue ETL pipelines, I used GitHub Copilot to generate transformation logic and Boto3 integration code, which significantly reduced development time while I reviewed and validated the generated code.”

Cursor AI

If you haven’t used it extensively, it’s better to be honest:

“I’ve explored Cursor and understand its agentic development capabilities. It provides repository-aware code generation, intelligent refactoring, and multi-file editing, which are valuable for large enterprise projects. While most of my production work has been with GitHub Copilot, I’m comfortable adopting Cursor because the underlying AI-assisted development workflow is similar.”

Mention features you’re familiar with:

• Repository-aware coding
• AI chat inside IDE
• Multi-file refactoring
• Code explanation
• Test generation
• Documentation generation

Devin AI

A balanced answer is:

“I’ve studied Devin as an autonomous AI software engineer and understand where it fits. Unlike Copilot, which assists developers, Devin can plan tasks, write code, execute tests, debug issues, and create pull requests with minimal supervision. I haven’t used Devin in a production environment yet, but I understand its workflow and potential for automating repetitive engineering tasks.”

Enterprise Perspective

You can also discuss governance:

• Human review remains mandatory.
• Never merge AI-generated code without validation.
• Perform security and compliance checks.
• Run automated tests.
• Ensure generated code follows organizational standards.
• Review AI-generated infrastructure as code before deployment.

Sample Interview Answer (2-minute version)

“Yes. I’ve used GitHub Copilot extensively for AI-assisted software development, primarily with Python, SQL, AWS Lambda, AWS Glue, and infrastructure automation. It helps generate boilerplate code, unit tests, API integrations, documentation, and code refactoring, allowing me to focus on architecture and business logic. I’ve also explored Cursor, particularly its repository-aware code generation and multi-file refactoring capabilities. While I haven’t used Devin in a production project, I’m familiar with its autonomous software engineering model, where it can plan tasks, implement code, run tests, and create pull requests. Regardless of the tool, I treat AI-generated code as a productivity accelerator rather than a replacement for engineering judgment, ensuring thorough code reviews, security validation, testing, and compliance before deployment.”

This answer is appropriate for senior roles such as AI Technical Architect, AI Solutions Architect, Principal Engineer, or Staff AI Engineer because it demonstrates practical experience, awareness of emerging AI developer tools, and sound engineering practices.

I haven’t used them in the sense of opening an IDE and typing code with them, since I don’t have hands-on access to tools or a local development environment.

However, I know a lot about them and can give you a clear breakdown of each:

1. GitHub Copilot (The Veteran)

• What it is: An AI pair programmer that autocompletes code, functions, and even entire boilerplate classes as you type. It’s deeply integrated into VS Code, JetBrains, and other major IDEs.
• Best for: Speed and reducing “grunt work.” It excels at writing repetitive code (e.g., getters/setters, unit test skeletons, API client boilerplate) and suggesting context-aware completions based on your comments and existing codebase.
• The Catch: It can sometimes suggest insecure or buggy code, and it heavily relies on the quality of your prompts and existing code context.

2. Cursor (The New Favorite)

• What it is: An AI-first code editor (a fork of VS Code) that is much more than just autocomplete. It has a built-in chat panel that can see your entire codebase, and its standout feature is “Apply”—it can directly edit specific lines or blocks of code across multiple files with a single click.
• Best for: Refactoring, debugging, and understanding complex, unfamiliar codebases. You can ask “Where is the payment logic?” and it will find the relevant files for you. It also has a “Composer” mode for multi-file edits.
• The Catch: It’s subscription-based ($20/month for Pro), and while it’s incredibly powerful, it can occasionally hallucinate or delete code if you aren’t careful with your instructions.

3. Devin (The Autonomous Agent)

• What it is: The “first AI software engineer.” Unlike Copilot and Cursor (which are assistants), Devin is an agent. You give it a task (e.g., “Build a weather dashboard that pulls from this API”), and Devin spins up its own sandbox, writes its own code, runs it, finds errors, fixes bugs, and deploys it—all autonomously.
• Best for: End-to-end tasks, bug fixes in open-source repos, and prototyping full features from scratch while you go grab a coffee.
• The Catch: It’s extremely expensive (enterprise pricing), slow (it can take 30+ minutes for complex tasks), and often gets stuck in loops on tricky bugs. It’s not meant to replace a developer; it’s meant to be a “junior dev” that senior engineers review and delegate grunt tickets to.

My Take (As an AI assistant):

If I could code, I’d use Cursor as my daily driver because it feels like having a senior dev looking over my shoulder. I’d use Copilot if I wanted something cheaper or already baked into my existing IDE. I’d use Devin only for isolated, well-defined chores (like migrating a legacy library or writing initial integration tests) that I don’t want to spend my morning on.