AI / ML Engineer

What matters most

• Production-Ready AI Pipeline Development

  The core requirement is to design and implement robust, scalable application pipelines that integrate cloud-based AI models. You must demonstrate the ability to move beyond experimental code to production-grade systems using Azure AI services, ensuring reliability and performance in real-world environments.

• Advanced LLM and Agentic Architecture

  You are expected to be a subject matter expert in RAG, Agentic RAG, and Agent-to-Agent (A2A) patterns. Prioritize demonstrating deep technical proficiency in orchestrating complex AI workflows using frameworks like LangChain, LangGraph, and MCP to solve business problems.

• Technical Leadership and Mentorship

  As an SME, you must balance individual development with cross-team collaboration. You will be responsible for making key architectural decisions and mentoring junior staff, requiring you to articulate complex technical strategies to both technical and non-technical stakeholders.

Likely challenges to think through

• Balancing Rapid AI Innovation with Enterprise Stability

  The role requires integrating fast-evolving technologies like LLMs and Agentic RAG into stable, production-ready pipelines. Prepare by discussing how you manage technical debt and model evaluation strategies to ensure that cutting-edge AI features do not compromise the stability of existing enterprise systems.

• Cross-Team Architectural Alignment

  You will be expected to influence decisions across multiple teams. The challenge lies in achieving consensus on technical standards and workflows in a large organization. Prepare by highlighting your experience in driving technical alignment and managing stakeholder expectations during complex deployment cycles.

Topics to prepare

• Retrieval-Augmented Generation (RAG) Architecture

  Focus on the end-to-end flow of RAG, specifically Agentic RAG and A2A (Agent-to-Agent) patterns. Exercise: Design a system architecture diagram for an Azure-based RAG pipeline that incorporates real-time data integration and explain how you would handle context window limitations using specific chunking strategies.

• Model Context Protocol (MCP) and Function Calling

  Understand how MCP standardizes communication between AI models and external data sources. Exercise: Create a mock implementation using Python or Java where an LLM uses function calling to retrieve data from an external API, demonstrating how you would validate the tool output using Pydantic models.

Skills to revise

• Azure AI Services Integration

  Hands-on proficiency with Azure OpenAI and Azure AI Search is a core requirement. Exercise: Build a small-scale application that indexes a set of documents into Azure AI Search and performs a semantic search query to provide context to an Azure OpenAI model.

• AI Framework Proficiency (LangChain/Spring AI)

  The role requires integrating models using frameworks like LangChain or Spring AI. Exercise: Develop a simple chatbot pipeline using LangChain or Spring AI that maintains conversation history and uses a vector database to retrieve relevant information before generating a response.

• Prompt and Context Engineering

  Optimization of model outputs is critical for production-ready systems. Exercise: Take a complex prompt and refactor it using few-shot prompting and chain-of-thought techniques, then document how you would measure the improvement in output accuracy or relevance.

• Prepare a technical case study on Azure-based RAG

  Document a project where you implemented Retrieval-Augmented Generation using Azure OpenAI and Azure AI Search. Be prepared to explain your specific chunking strategies, vector database selection, and how you handled real-time data integration to ensure production-ready quality.

• Demonstrate proficiency in Agentic workflows

  Create a brief technical walkthrough or code sample demonstrating Agentic RAG, function calling, or Agent-to-Agent (A2A) communication. Highlight your experience with frameworks like LangGraph or LlamaIndex to manage complex, multi-step AI application pipelines.

• Review your experience with Java and Python AI frameworks

  Prepare to discuss your hands-on experience integrating AI models using Spring AI for Java and Python-based libraries like LangChain, Pydantic, and TensorFlow. Focus on how you have optimized these models for deployment in cloud environments.

• Develop examples of model evaluation and optimization

  Prepare evidence of how you have measured and improved model performance. This should include your approach to prompt engineering, context engineering, and the metrics used to evaluate output quality in production-scale applications.

• Formulate leadership and mentorship scenarios

  Prepare specific examples from your 5+ years of experience where you acted as a subject-matter expert, mentored junior developers, or made key architectural decisions that impacted multiple teams. Focus on how you improved development workflows or resolved cross-team technical challenges.

• Prepare strategic questions for the interviewers

  Draft thoughtful questions regarding the team's current AI pipeline architecture, how they manage the lifecycle of generative AI models in Azure, and the specific challenges they face when scaling Agentic RAG solutions in a production environment.