Persistent Systems
Engineering-services firm pushing GenAI and agentic AI work for clients
Interview in a few hours? A quick sprint for Persistent Systems
AI in interviews
No public policy found. Assume no AI assistance in live rounds unless your recruiter says otherwise.
Typical interview loop
How Persistent Systems usually runs it for applied AI and LLM engineer roles.
- Focus: CS basics and aptitude
Online assessment
A timed online test covering computer-science fundamentals, logical reasoning and English.
- Focus: Coding and problem solving
Coding round
Timed coding problems, with a harder optional round for top scorers in campus drives.
- Focus: Projects, coding, databases
Technical interviews
Usually one or two rounds that start from your resume and projects, then move to coding, SQL and data-structure questions.
- Focus: Fit and motivation
HR discussion
A final conversation on background, motivation and fit after the technical interview.
Topics to prepare for Persistent Systems
The topics its loop weighs most, each with what interviewers check and what to study.
- Python for ML interviewsCoding
- Calling LLM APIs in Python: async, streaming, retriesCoding
- Embeddings basics: word2vec, contrastive learning, what embeddings captureML fundamentals
- Self-attention from scratchTransformers
- Retrieval for RAG: chunking, hybrid search, rerankingRAG
- Evals and error analysisEvals
- The decoder-only transformer (GPT forward pass)Transformers
- Decoding: greedy, beam, temperature, top-k/top-pInference
How it weights your plan
Selecting Persistent Systems when you build a plan multiplies each topic’s priority score:
Official prep and resources
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