AI / ML Engineer

What matters most

• End-to-End Generative AI Lifecycle Management

  The role requires owning solutions from initial design through to production deployment. You must demonstrate the ability to architect RAG systems, manage data ingestion pipelines, and implement robust evaluation frameworks to ensure model performance in real-world environments.

• Scalable Azure-Native LLM Integration

  You are expected to build and deploy applications on Azure using services like App Service, AI Search, and Cosmos DB. A core priority is managing the technical complexities of LLM integration, specifically handling API rate limits, implementing fault-tolerant retries, and ensuring load balancing for asynchronous REST APIs.

• Technical Leadership and Mentorship

  Beyond individual development, you are responsible for supporting team delivery and mentoring junior colleagues. This requires the ability to communicate complex architectural decisions clearly and foster technical growth within the team while maintaining high production standards.

Likely challenges to think through

• Balancing Rapid Prototyping with Production Stability

  The requirement to deliver PoCs and MVPs under tight timelines often conflicts with the need for production-grade reliability, such as implementing comprehensive CI/CD, logging, and monitoring via OpenTelemetry. Prepare by discussing strategies for building modular, extensible code that allows for quick iteration without sacrificing long-term maintainability.

• Managing Non-Deterministic LLM Outputs in Production

  Deploying generative AI systems involves inherent challenges regarding output consistency, latency, and cost management. Prepare by detailing your experience with prompt engineering, vector search optimization, and evaluation metrics that mitigate hallucinations and ensure system reliability in high-stakes production environments.

Topics to prepare

• RAG System Architecture and Evaluation

  Focus on the end-to-end pipeline from data ingestion and preprocessing to vector search and retrieval. Exercise: Design a document-update pipeline that handles versioning and evaluate retrieval accuracy using metrics like precision at k or RAGAS, as the role requires owning RAG solutions from design to production.

• Production-Grade LLM Application Resilience

  Understand strategies for managing LLM API constraints. Exercise: Draft a technical plan for implementing exponential backoff, circuit breakers, and load balancing for asynchronous API calls, specifically addressing how to maintain fault tolerance when integrating with Azure OpenAI or similar platforms.

Skills to revise

• Asynchronous Python API Development

  Develop high-performance REST APIs using FastAPI or Flask. Exercise: Build a mock asynchronous endpoint that processes multiple concurrent LLM requests, implementing proper error handling and logging using OpenTelemetry to ensure production-readiness.

• Azure Cloud Deployment and Monitoring

  Master the deployment of AI services on Azure. Exercise: Create a CI/CD pipeline configuration using Jenkins that deploys a containerized Python application to Azure App Service, including the setup of monitoring hooks for tracking application health and latency.

• Agentic Workflow Implementation

  Utilize agentic libraries to build complex AI assistants. Exercise: Build a simple multi-agent workflow using LangGraph or AutoGen that delegates tasks between a researcher agent and a writer agent, demonstrating your ability to handle complex orchestration beyond basic prompt engineering.

• Prepare a production-grade RAG architecture walkthrough

  Draft a technical summary of a generative AI system you have deployed. Focus on your approach to data ingestion, vector database selection, and how you managed LLM API constraints like rate limiting and fault tolerance in a production environment.

• Demonstrate asynchronous API design proficiency

  Prepare code snippets or a high-level design document showing how you implemented asynchronous REST APIs using FastAPI or Flask. Highlight how you handled concurrency and integrated monitoring tools like OpenTelemetry to track performance.

• Map Azure service expertise to the role requirements

  Create a brief technical document outlining your experience with Azure App Service, AI Search, and Cosmos DB. Explain how you have utilized these specific services to build scalable, cloud-native AI applications.

• Develop a strategy for LLM evaluation and monitoring

  Prepare a discussion point on your methodology for evaluating LLM outputs, including how you measure accuracy, latency, and cost. Be ready to explain how you implement logging and monitoring to ensure system reliability post-deployment.

• Formulate targeted questions for the interviewers

  Prepare questions regarding the team's current CI/CD pipeline for AI models, their approach to managing agentic workflows using libraries like LangGraph or AutoGen, and how they balance rapid MVP delivery with long-term production maintenance.