CTO → Developer Interview Checklist (AI, Cloud, Software Dev) – 2025 Edition

Format: Ready-to-use during live interviews or as a scorecard. Each item = a critical signal.

🧠 AI & Copilot Proficiency

Goal: Assess whether a candidate is AI-native or stuck in 2022

• Can you walk me through how you use GitHub Copilot or an alternative in daily development?

• What’s your most effective prompting strategy for generating or refactoring code?

• Can you show me a real PR where Copilot saved you hours?

• Have you used Cursor IDE, Codeium, or other AI-native IDEs?

• Do you use AI to write or auto-generate unit tests? (Bonus: With coverage reports)

☁️ Cloud-Native Readiness

Goal: Confirm they can build, deploy, and scale on modern cloud stacks

• Which cloud providers have you used in production? (AWS, Azure, GCP?)

• Have you deployed AI/ML models with SageMaker, Vertex AI, or Azure ML Studio?

• Can you describe a CI/CD pipeline you’ve built or maintained?

• Are you comfortable writing Infrastructure-as-Code (IaC) with Terraform?

• How would you scale an LLM-powered backend in the cloud under GPU constraints?

🤖 LLM API Integration & Prompt Engineering

Goal: Determine if they know how to work with LLMs beyond chat experiments

• What’s your experience with OpenAI, Claude, or LLaMA 2 APIs?

• Have you built a product feature or prototype using an LLM?

• Can you explain a use case where you implemented RAG (Retrieval-Augmented Generation)?

• What LLM orchestration tools have you used? (LangChain, LlamaIndex, Semantic Kernel?)

• Do you understand token limits, embedding vectors, and how to fine-tune prompts?

🔐 Security & Code Quality with AI

Goal: Ensure AI doesn’t introduce security/tech debt

• How do you validate or audit AI-generated code?

• Do you use tools like Snyk, SonarQube, or DeepCode?

• What’s your process for reviewing Copilot-generated pull requests?

• Can you give an example of a security bug introduced via AI?

🧪 AI in Testing & Debugging

Goal: Evaluate how they use AI to speed up QA/dev cycles

• Have you used AI tools to write or refactor test suites?

• How do you handle flaky tests or test coverage gaps?

• What tools do you use to debug faster with AI assistance?

📊 Data Analysis & Visualization (for AI/Backend Roles)

Goal: Gauge data comfort and practical AI usage

• Have you used Pandas AI, Polars + ChatGPT, or Data Explorer tools?

• Can you describe a time when AI helped you analyze or visualize a dataset faster?

🧰 Personal Productivity Stack (Bonus Points)

Goal: Detect high-efficiency habits and personal workflows

• Which AI tools do you use to manage time, docs, or planning? (Notion AI, Raycast, Whimsical?)

• How do you track or document architecture and dev decisions with AI?

❌ Red Flags to Watch Out For

Statement

🚨 Signal

“I haven’t tried GitHub Copilot yet.” 👉 Not AI-native

“I only use ChatGPT to explain things.” 👉Passive user

“I prefer writing everything manually.” 👉Low efficiency mindset

“I don’t trust AI in production code.” 👉Stuck in legacy thinking

“I’ve read about LLMs but never used APIs.” 👉No real-world exposure