Iris software GenAI Engineer interview questions & guide 2026

The following questions reflect the core competencies we look for in our GenAI Engineer candidates. While interviewers may adapt these based on your specific background, the focus remains on your ability to apply theoretical AI concepts to real-world software architecture.

RAG and LLM Fundamentals

These questions test your foundational knowledge of how LLMs operate and how to augment them with external data.

• Explain the architecture of a robust RAG system and how you handle data retrieval latency.
• How do you evaluate the performance of an LLM-based application beyond simple accuracy metrics?

Preparation at Iris software should be structured around demonstrating both depth in AI theory and a pragmatic approach to software engineering. You are being evaluated on your ability to bridge the gap between "research-level" AI and "enterprise-grade" software.

Role-related knowledge – You must demonstrate a deep understanding of LLM lifecycle management, including fine-tuning versus prompting strategies and RAG optimization. Be prepared to discuss specific libraries, frameworks, and vector databases you have utilized in previous projects.

Problem-solving ability – We look for candidates who can take an ambiguous requirement and break it down into a testable, scalable system design. Show us how you prioritize trade-offs between latency, cost, and output quality.

Communication – As a lead-level role, you must be able to articulate your architectural choices clearly. We value candidates who can explain why a specific model or vector strategy is the right fit for the specific business problem at hand.

The interview process at Iris software is designed to be streamlined, focusing on your technical fluency and your ability to work within a product-focused team. You can expect a sequence that moves from initial technical screening to deeper architectural discussions. We value directness and transparency; you will find that our interviewers are collaborative and focused on understanding your thought process rather than testing rote memorization.

This timeline outlines the typical path from your initial technical screen to the final round. Use this to pace your preparation; the earlier stages are typically high-level technical checks, while later rounds are deeper dives into system design and behavioral fit. Plan to spend your energy on mastering the fundamentals of RAG and system-level integration.

RAG and LLM Implementation

This is the heart of the role. You will be expected to demonstrate how you build systems that provide accurate, grounded responses.

• Data Chunking and Embedding – Understanding the impact of different chunking strategies on retrieval quality.
• Vector Database Management – Knowledge of indexing strategies and query optimization.
• Evaluation Frameworks – Familiarity with tools used to score model outputs (e.g., faithfulness, relevance).

Example scenarios:

• "Walk me through how you would optimize a RAG pipeline that is returning irrelevant context."
• "How do you handle multi-hop reasoning in a retrieval-augmented system?"

As a GenAI Engineer, your primary objective is to ensure that the GenAI features developed at Iris software are not only innovative but also stable and performant. You will spend a significant portion of your time designing and maintaining automated testing suites specifically for non-deterministic AI outputs. This involves building evaluation harnesses that simulate user interactions and measure the quality of LLM responses against ground-truth datasets.

Beyond testing, you will act as a bridge between the data science team and the product engineering team. You will influence the architectural decisions regarding model selection, API integration, and latency management. You will frequently be tasked with conducting root-cause analysis on AI-driven feature failures, requiring a deep dive into logs, trace data, and model parameters to identify whether the issue lies in the retrieval, the prompt, or the model itself.

A competitive candidate for this position brings a combination of rigorous software engineering discipline and contemporary AI experience.

• Must-have skills:
• Proficiency in Python and experience with LLM frameworks (such as LangChain or LlamaIndex).
• Deep experience with vector databases (e.g., Pinecone, Milvus, or Weaviate).
• Hands-on experience with RAG architecture and evaluation metrics.
• Strong grasp of CI/CD pipelines for AI models.
• Nice-to-have skills:
• Experience with model quantization or optimization for inference latency.
• Knowledge of fine-tuning techniques like PEFT or LoRA.
• Prior experience in an SDET or QA Automation capacity within an AI-heavy environment.

Q: Is the interview process mostly theoretical or practical? A: It is highly practical. We focus on how you solve real problems, so be ready to discuss your past projects and the specific "why" behind your technical decisions.

Q: How much weight is placed on coding versus system design? A: For the GenAI Engineer role, system design—specifically how you structure AI pipelines—is prioritized. You should be prepared to sketch out architectures and explain the trade-offs of your design choices.

Q: What is the culture like at Iris software? A: We are a product-focused organization that values engineering excellence and collaborative problem-solving. We look for candidates who are intellectually curious and willing to challenge the status quo.

• Structure your answers: Use the STAR (Situation, Task, Action, Result) method, but ensure the "Action" portion focuses heavily on the technical decisions you made regarding GenAI components.
• Be honest about limitations: If you haven't used a specific tool, explain how you would go about learning it or how you would apply a similar concept from a tool you do know.
• Focus on reliability: Since this role involves significant testing responsibilities, emphasize your mindset toward "defensive engineering"—how you build systems that fail gracefully and are easy to debug.

The GenAI Engineer position at Iris software is an opportunity to shape the future of our product intelligence. By focusing your preparation on RAG fundamentals, system design for non-deterministic models, and a robust approach to testing, you will be well-positioned to excel in our interview process.

We encourage you to review your past projects through the lens of scalability and reliability. You have the technical foundation; now, demonstrate how you apply it to build world-class software. For additional insights and to track your progress, continue utilizing our internal resources. Good luck—we look forward to seeing your expertise in action.

This module provides a benchmark for the compensation range for this role in India. Use this data to understand the market expectation and to ensure your own expectations are aligned with the seniority and responsibility level of a Lead position.