IBM
Global technology and consulting firm with deep roots in enterprise IT and AI.
3 Rounds
~14 Days
Medium
The Interview Loop
Recruiter Screen (30 min)
Standard fit check, behavioral questions, and resume overview.
Technical Loop (3-4 Rounds)
Deep dive into domain knowledge, coding, and system design.
Interview Question Bank
Machine Learning Engineer
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Technical
•
medium
How does Retrieval-Augmented Generation (RAG) work, and what are the key components needed to build a RAG pipeline using WatsonX or LangChain?
#RAG
#LLMs
#Vector Databases
Machine Learning Engineer
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Technical
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hard
What metrics would you use to evaluate the quality and safety of a Generative AI model's text output?
#Model Evaluation
#LLMs
#AI Safety
Machine Learning Engineer
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Technical
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medium
What are the trade-offs between fine-tuning an open-source LLM (like Llama 3 or Granite) versus using a RAG approach?
#Fine-tuning
#RAG
#LLMs
Machine Learning Engineer
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Technical
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hard
Explain the concept of LoRA (Low-Rank Adaptation) and how it reduces the computational cost of fine-tuning LLMs.
#PEFT
#LLMs
#Fine-tuning
Difficulty Radar
Based on recent AI-sourced data.
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The "Standard" Interviewer
Senior EngineerFocuses on core competencies, system constraints, and clear communication.
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Think Out Loud
Always explain your thought process before writing code or drawing architecture.