Citi

Typical interview loop

How Citi usually runs it for applied AI and LLM engineer roles.

1. 1

   Coding assessment

   Focus: Coding and problem solving

   After applying, tech candidates take an online coding assessment; other programmes use a skills survey instead.

2. 2

   First one-to-one interview

   Focus: Background and motivation

   A single interviewer explores your background and how your goals line up with Citi's.

3. 3

   Technical interview

   Focus: Projects, SQL, data design

   A technical round built around your projects, databases and SQL, sometimes with design or whiteboard coding.

4. 4

   Final stage

   Focus: Depth and business judgment

   Depending on programme and location, a set of further interviews, an assessment, or a case-study exercise.

5. 5

   HR and managerial panel

   Focus: Leadership and fit

   A panel covers leadership, hypothetical work scenarios and why you want to join.

Topics to prepare for Citi

The topics its loop weighs most, each with what interviewers check and what to study.

• Behavioral interviews: STAR stories for AI engineersBehavioral
• Python for ML interviewsCoding
• Embeddings basics: word2vec, contrastive learning, what embeddings captureML fundamentals
• Calling LLM APIs in Python: async, streaming, retriesCoding
• The project deep-dive roundBehavioral
• Story bankBehavioral
• Interview openers and closersBehavioral
• Retrieval for RAG: chunking, hybrid search, rerankingRAG

How it weights your plan

Selecting Citi when you build a plan multiplies each topic’s priority score: