AI / ML Engineer

What matters most

• Deliver production-ready AI pipelines

  The role calls for designing and implementing cloud or on-premises application pipelines and integrating generative AI models. Prepare to explain how you take an AI application from design through deployment and optimization.

• Build LLM applications on Azure

  LLMs are the required skill, and the posting seeks hands-on use of Azure OpenAI and Azure AI Search to develop and deploy AI applications at scale. Prepare a concrete architecture example that shows how the services fit together.

• Design retrieval and agentic workflows

  The posting names RAG, Agentic RAG, MCP, function calling and A2A, alongside embeddings, chunking and real-time data integration. Prepare to explain when a simpler retrieval design is sufficient and when additional orchestration is warranted.

• Evaluate and improve output quality

  High-quality outputs, model-output evaluation and optimization are explicit parts of the role. Prepare to describe how you assess results, diagnose failures and use findings to improve an application.

• Lead decisions across teams

  The engineer is expected to act as an SME, make team decisions, contribute to decisions across teams, mentor junior colleagues and improve workflows. Prepare examples that distinguish your technical decisions from your team-level contributions.

Likely challenges to think through

• Balancing pipeline reliability with rapid iteration

  Likely preparation challenge, not a stated employer problem: generative AI components can change while the role still demands production-ready pipelines. Prepare to discuss how you would validate changes and maintain output quality during deployment and optimization.

• Choosing retrieval complexity deliberately

  Likely preparation challenge: chunking, embeddings and real-time data integration create design choices, while agentic patterns add moving parts. Prepare to compare a basic RAG approach with an agentic one using retrieval quality, data freshness and ease of evaluation as criteria.

• Making decisions across team boundaries

  Likely preparation challenge: the role combines hands-on engineering, team decisions, cross-team input and mentoring. Prepare an example of resolving competing technical preferences while keeping the implementation and team workflow clear.

Topics to prepare

• LLM application design

  The posting names LLMs as the must-have skill and calls for production-ready generative AI applications. Revise how prompts, context, model integration and application pipelines affect output quality; sketch a design and explain its failure modes.

• Retrieval architecture

  The posting explicitly names RAG, vector databases, embeddings and chunking. Compare two chunking strategies for the same document set, then explain how each could affect retrieval relevance and answer quality.

• Agentic integration patterns

  The posting lists Agentic RAG, MCP, Functional Calling and A2A. Draw an interaction flow and explain when each pattern would be useful, where control passes between components and how you would handle a failed call.

• Azure AI deployment architecture

  The posting calls for Azure OpenAI, Azure AI Search and cloud-based integration, deployment and optimization at scale. Diagram a production-ready pipeline using those services, including where real-time data enters and where outputs are checked.

• Output evaluation and optimization

  Evaluating model output and optimizing it are explicit requirements. Define a small set of test questions and describe how you would assess answer quality, diagnose weak results and compare a revised configuration against the original.

• Technical decisions across teams

  The role expects an SME who makes team decisions, contributes across teams, mentors juniors and improves workflows. Rehearse explaining an AI architecture trade-off, the evidence behind your decision and how you would help another team adopt it.

Skills to revise

• Implement model integration in Python or Java

  The posting requires experience in both languages and lists frameworks including Spring AI and LangChain. As a revision exercise, write a small model-integration flow in one language, then outline how you would implement the same flow in the other; explain your framework choice without treating every listed framework as mandatory.

• Build an Azure-backed retrieval flow

  Practise connecting Azure AI Search retrieval to an Azure OpenAI response, with document ingestion, citations and a fallback for missing evidence. This exercises the posting’s hands-on Azure requirements and RAG architecture; if services are unavailable, prepare the integration steps and interface code for review.

• Tune prompts and retrieved context

  Given a few weak answers, revise the prompt and the selection of retrieved chunks, then compare the results against the originals. This directly practises the posting’s prompt engineering, context engineering and embedding/chunking requirements.

• Test and improve model outputs

  Create a compact test set with expected answer characteristics, run it against two configurations and record which errors changed. This prepares for the stated responsibility to evaluate outputs and optimize AI applications.

• Lead a design review

  As interview preparation, practise a short review of your pipeline design: present alternatives, invite objections, decide on a path and explain one workflow improvement to a junior colleague. This is a suggested exercise for the posting’s team-decision, mentoring and process-improvement responsibilities.

• Map the stated requirements

  Prepare a factual timeline of LLM and AI application work, plus education details, against the posting’s minimum five years with LLMs and 15 years of full-time education. Note evidence you can provide without claiming experience you do not have.

• Build a production-project walkthrough

  Choose a project you can discuss and prepare a diagram showing the application pipeline, model integration, deployment and optimization. Identify your own decisions and outcomes; the role calls for production-ready cloud or on-premises systems.

• Practice an Azure RAG design exercise

  As preparation, sketch an application using Azure OpenAI and Azure AI Search. Explain choices for retrieval, embeddings, chunking, context and data updates, then discuss when Agentic RAG, function calling, MCP or A2A would—or would not—help.

• Prepare implementation evidence

  Gather a small code sample or pseudocode you can explain in Java or Python. Show how you integrated a model using a framework you have actually used, such as Spring AI, LangChain or LangGraph, and distinguish hands-on work from familiarity.

• Show how you evaluate outputs

  Prepare an example of checking model-output quality, investigating failures and making an optimization decision. A sample test set and before-and-after results are useful preparation if you have them; the posting specifies evaluation and optimization but not a particular metric.

• Document leadership decisions

  Outline one example each of a team decision, cross-team problem, junior-colleague mentoring and workflow improvement. State the situation, your contribution, the trade-off and the result, reflecting the role’s SME and team responsibilities.

• Write questions for the employer

  Ask which AI applications are priorities, where Azure versus on-premises pipelines are used, how output quality is evaluated, and what decisions the AI / ML Engineer owns across teams. These are questions to clarify the role, not assumptions about its projects.