Langchain India AI Research Engineer Interview: Rounds, Questions and Preparation

What the job involves

AI Research Engineers at fast-growing deeptech companies like Langchain India work on building and evaluating LLM-powered applications, often focusing on retrieval-augmented generation, agent frameworks, and prompt optimization. They prototype new model pipelines, run experiments, and collaborate with product teams to ship research into production. The role blends deep learning fundamentals with rapid software engineering in Python.

Role and technical knowledge

1. 1

   Explain how retrieval-augmented generation (RAG) works and why it is used in LLM applications.

   Cover the retriever, vector store, and generator pipeline. Mention how RAG reduces hallucinations and allows updating knowledge without retraining.

2. 2

   What are the key differences between fine-tuning an LLM and using prompt engineering? When would you choose one over the other?

   Discuss cost, data requirements, latency, and task specificity. Fine-tuning is better for domain adaptation with ample data; prompting for quick, low-resource tasks.

3. 3

   How do you evaluate the performance of an LLM-based system beyond accuracy?

   Mention metrics like BLEU, ROUGE, perplexity, and human evaluation. Also cover retrieval metrics (precision, recall) and end-to-end task success.

4. 4

   Describe the transformer architecture and how self-attention works.

   Explain queries, keys, values, multi-head attention, and positional encodings. Highlight why it scales better than RNNs for long sequences.

Practical scenarios

1. 5

   You are building a chatbot for internal company documents. How would you design the retrieval system to handle thousands of PDFs?

   Discuss chunking strategies, embedding models, vector database choice (e.g., FAISS, Pinecone), and metadata filtering. Mention handling tables and images.

2. 6

   Your RAG pipeline returns irrelevant answers. How would you debug and improve it?

   Check retrieval quality first: embedding model, chunk size, and similarity metric. Then evaluate prompt and LLM. Suggest hybrid search or re-ranking.

3. 7

   How would you reduce the latency of an LLM agent that makes multiple API calls?

   Cover caching, parallel tool calls, smaller models for routing, and streaming responses. Mention trade-offs with accuracy.

4. 8

   You need to fine-tune a 7B parameter model on a single GPU. What techniques would you use?

   Mention LoRA, QLoRA, gradient checkpointing, and mixed precision. Discuss dataset size and evaluation to avoid overfitting.

Behavioural and HR

1. 9

   Why do you want to work at a fast-growing AI startup like Langchain India instead of a large tech company?

   Show passion for rapid iteration, ownership, and impact. Connect to your interest in LLMs and shipping research quickly.

2. 10

   Tell me about a time you had to learn a new AI framework or tool quickly for a project.

   Use STAR method. Highlight self-learning, documentation reading, and applying it to deliver results under time pressure.

3. 11

   Describe a situation where your experiment failed. What did you learn?

   Focus on the learning process, debugging steps, and how you iterated. Show resilience and scientific thinking.

4. 12

   How do you stay updated with the latest AI research, and how would you bring that into your work?

   Mention arXiv, Twitter, and blogs. Give an example of a recent paper you implemented or a technique you tried.

Coding and problem solving

1. 13

   Write a Python function to compute cosine similarity between two vectors without using NumPy.

   Show understanding of dot product and magnitude. Handle edge cases like zero vectors.

2. 14

   Given a list of documents, implement a simple keyword-based retriever that returns the top 3 most relevant documents for a query.

   Use TF-IDF or BM25. Explain tokenization, scoring, and sorting. Mention limitations compared to embeddings.

3. 15

   How would you split a long document into chunks for embedding? Write pseudocode for a chunking function.

   Consider fixed-size with overlap, sentence-based, or semantic chunking. Discuss trade-offs between context and retrieval precision.

4. 16

   Implement a function to parse a JSON response from an LLM and extract a specific field, with error handling.

   Use try-except, validate schema, and handle missing keys. Mention using Pydantic or similar for robust parsing.