Pocketpills currently has 3 live openings on OnJob.io, concentrated in AI Engineer, Backend Developer and UI/UX Designer roles. There are no leaked or company-specific questions here — prepare with the general, role-based interview rounds and most-asked questions for those role types below.
OnJob.io does not publish leaked or company-confidential interview questions. The guides below are general, role-based preparation — the standard rounds and commonly asked questions for the role types Pocketpills is currently hiring for. Your real Pocketpills interview may differ.
What to expect when interviewing for AI Engineer, Backend Developer and UI/UX Designer roles at companies like Pocketpills
Pocketpills is currently advertising these role types on OnJob.io. Each guide is the general interview structure and most-asked questions for that role.
General AI Engineer interview prep
1 live role at Pocketpills
LLMs, prompt engineering, RAG and AI application development — the fast-growing generative-AI engineering interview at Indian product startups, GCCs and AI-first companies. Distinct from a classic ML engineer: the focus is building on top of foundation models, not training them.
Typical AI Engineer interview rounds
01
LLM & GenAI fundamentals
How transformers and LLMs work, tokens, embeddings, context windows and prompting.
02
RAG & application design
Retrieval-augmented generation, vector databases, chunking and grounding.
03
Coding round
Python coding plus building an API around an LLM with a framework like LangChain.
04
System design & evaluation
Designing a production AI feature, handling cost, latency, safety and evaluation.
Commonly asked AI Engineer questions
What is the difference between fine-tuning, prompt engineering and retrieval-augmented generation?
Explain how RAG works end to end, from a user query to a grounded answer.
What are embeddings and how are they used in semantic search?
What is a vector database and how does similarity search work?
What is a context window, and how do you handle inputs that exceed it?
What causes hallucinations in LLMs and how do you reduce them?
What is the difference between temperature and top-p sampling?
How would you chunk documents for a RAG pipeline, and why does chunk size matter?