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American Express interview notes, page 1 of 2

Large Enterprises

Screna has 15 American Express interview write-ups. The most reported round is the technical phone screen (7).

15 interview notes · updated Jul 2026

American Express·Product Manager·Hiring Manager Screen

Apr 2026
Interviewed for a PM role at American Express and got hit with a classic PM vs PO distinction question. Nothing too wild but it's the kind of thing that trips you up if you haven't thought carefully about where the lines actually blur in practice.
  • What is the difference between a product manager and a product owner?

“I gave the textbook answer about PMs owning strategy and POs managing the backlog and working closer with engineering.”

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American Express·AI Engineer·Technical Phone Screen

Apr 2026
Short Karat-style debugging round for an AI Engineer role at American Express. Two parts: find and fix a bug in existing Python code by reading failing test output, then write a new function to make more tests pass. The whole thing was 20 minutes so speed of diagnosis mattered more than anything else.
  • You're given a set of Python classes and a failing test suite. Read the test output, identify which function contains the bug, explain why the test is failing, and fix it with a minimal change.
  • Given a short spec, write a new function from scratch so that a set of currently-failing tests will pass.

“The tricky part isn't writing code, it's resisting the urge to rewrite things.”

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American Express·AI Engineer·Onsite - System Design / Architecture

May 2026
Thirty-minute system design case at Amex for an AI Engineer role. The whole thing was a deep dive on building a RAG pipeline end to end, from raw document ingestion all the way through evaluation and ops. Dense but interesting.
  • Design a Retrieval-Augmented Generation system over a corporate document corpus. Walk through the full pipeline from ingestion to generation, including retrieval strategy, evaluation, and operational concerns.
  • How would you handle multi-tenant access control when users should only retrieve documents they're authorized to see?
  • What chunking strategy would you use and why? How does chunk size affect retrieval quality?
  • How would you evaluate the RAG system, specifically around faithfulness and answer quality?
  • How would you keep the index fresh as documents are updated or deleted in the corpus?

“This is a big open-ended one and I think I frontloaded too much time on chunking strategy and not enough on the access control piece, which came up later as a follow-up and I was a bit scrambled.”

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American Express·AI Engineer·Onsite - System Design / Architecture

May 2026
System design round at Amex for an AI Engineer role, focused almost entirely on concurrency in multi-user agent systems. Pretty deep technically and covered a lot of ground in one question.
  • How would you design a multi-user AI agent system to handle concurrency safely? Walk through context isolation per user, shared resource management (model servers, vector stores, tool backends), fair queueing, request batching vs. latency SLAs, session memory isolation, and consistency when multiple sessions write to shared agent state. Also cover observability and how you'd test for concurrency bugs.

“This was a lot to unpack in one question.”

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American Express·Product Manager·Recruiter / HR Screen

May 2026
Short PM screen at American Express, basically just one question about motivation for the role.
  • Why do you want to work at American Express?

“Pretty standard opener.”

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American Express·Product Manager·Onsite - Product Sense / Strategy

May 2026
PM interview at American Express, one design question about building a reimbursement system from scratch. Pretty open-ended, which I wasn't fully prepared for.
  • Design an automated reimbursement system.

“I went straight into features and kind of skipped the problem framing, which I regretted about two minutes in.”

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American Express·Software Engineer·Technical Phone Screen

May 2026
Interviewed for a data science role at American Express and got a pretty core ML theory question about Random Forest. Nothing too wild but it made me realize how much I take ensemble methods for granted without being able to explain them cleanly under pressure.
  • What is the key mechanism behind Random Forest, and what role does bootstrapping play in how the algorithm works?

“I knew the general idea but stumbled trying to explain bootstrapping precisely.”

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American Express·Product Manager·Hiring Manager Screen

May 2026
Interviewed for a PM role at American Express, pretty standard stuff from what I can tell.
  • Where do you see your career going in the next few years?

“Classic question but I always fumble it a little.”

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American Express·AI Engineer·Technical Phone Screen

May 2026
Technical screen for an AI Engineer role at Amex. One meaty conceptual question about how LLMs, RAG, and agents relate to each other. Felt like they wanted to see if you actually understood the architecture or were just name-dropping buzzwords.
  • Walk me through the differences between a base LLM, a RAG system, and an autonomous agent. How do they relate to each other, when would you use each, and how do they compose together?

“This question is deceptively wide.”

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American Express·AI Engineer·Technical Phone Screen

Jun 2026
Amex AI Engineer interview that went deep on LLM security, specifically prompt injection. The whole session felt like one long threat modeling exercise and I was not fully prepared for how far into the weeds they wanted to go on mitigations.
  • What is the difference between direct prompt injection and indirect prompt injection, and why does the distinction matter for threat modeling?
  • Walk through the main threat categories in LLM agent systems, covering data exfiltration, privilege escalation, jailbreaks, and tool misuse.
  • What mitigation strategies would you apply to defend an LLM agent against prompt injection, and what are the limits of each?
  • How would you design an end-to-end defense for an LLM agent system deployed in a financial services context, assuming defense-in-depth?
  • How does content provenance help defend against indirect prompt injection, and where does it fall short?

“I explained direct as a user crafting malicious input themselves vs indirect as the attack arriving through retrieved content or tool output.”

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