Technical practice
Python Backend Framework Selection
What to coverCompare Flask, FastAPI, and Django in the context of building scalable AI-integrated services. Discuss when you would choose an asynchronous framework like FastAPI over a more monolithic approach like Django, specifically regarding performance and developer velocity.
Asynchronous Programming in Python
What to coverExplain the role of the 'asyncio' library in handling high-concurrency tasks for voice and chat applications. Describe how you would manage non-blocking I/O operations when integrating external LLM APIs to ensure the system remains responsive.
MongoDB and Redis for AI Workflows
What to coverDetail how you would architect a storage and caching strategy using MongoDB and Redis for an LLM-powered application. Focus on how Redis can be utilized for session management or caching prompt responses to reduce latency.
Prompt Engineering Strategies
What to coverExplain your methodology for designing and refining prompts for Generative AI models. Discuss techniques like few-shot prompting, chain-of-thought, or system-level instructions to improve the accuracy and reliability of model outputs in enterprise use cases.
Integrating LLMs into REST APIs
What to coverDescribe the process of wrapping an LLM inference call within a RESTful API endpoint. Address concerns such as handling long-running requests, managing API rate limits, and ensuring secure transmission of sensitive enterprise data.
System Scalability and Reliability
What to coverHow would you design a backend system to handle sudden spikes in traffic for a voice-based enterprise solution? Suggest strategies for horizontal scaling, load balancing, and implementing robust error handling for external service failures.
Debugging AI-Powered Applications
What to coverDiscuss the challenges of debugging non-deterministic outputs from LLMs. Suggest a workflow for logging, monitoring, and evaluating model performance to ensure consistent behavior in production environments.
Data Serialization and JSON Handling
What to coverExplain best practices for handling complex JSON structures when communicating between your backend and AI models. Discuss how to validate data schemas to prevent runtime errors during integration.
Behavioural practice
Adapting to High-Intensity Environments
What to coverDescribe a situation where you had to work under tight deadlines in a fast-growing startup environment. Explain how you prioritized tasks and maintained code quality while delivering results.
Cross-Functional Collaboration
What to coverProvide an example of a time you collaborated with AI/ML researchers to deploy a model into production. How did you bridge the gap between model development and backend infrastructure requirements?
Handling Technical Ambiguity
What to coverTell me about a time you were tasked with integrating a new, unfamiliar technology or API. What steps did you take to learn the tool quickly and successfully implement it into your project?
Problem-Solving in Production
What to coverDescribe a scenario where a critical system failure occurred in a production environment. Explain your process for identifying the root cause, communicating with stakeholders, and implementing a permanent fix.