Infosys GenAI Engineer interview questions & guide 2026

The questions you will encounter during the Infosys hiring process are designed to evaluate both your theoretical understanding of generative AI and your hands-on engineering capabilities. While the exact questions will vary depending on the specific business unit and project requirements, they consistently follow key patterns focused on practical system design and core AI concepts.

LLM & GenAI Core Concepts

These questions assess your fundamental understanding of how Large Language Models operate, their limitations, and the underlying mechanics of natural language processing.

• What is an LLM, and how does it differ from traditional machine learning models?
• Explain the concept of tokenization. How do tokenizers handle out-of-vocabulary words, and how does token count impact cost and latency?

Preparing for a GenAI Engineer interview at Infosys requires a balanced approach. You must demonstrate that you are not only a competent software engineer but also a specialist who understands the nuances of modern AI systems.

Role-Related Knowledge – You must show a deep, intuitive understanding of generative AI concepts. Do not just memorize definitions; be prepared to explain why you would choose a specific model, embedding, or chunking strategy over another. Interviewers will push you on the trade-offs of your technical choices.

System Architecture & Integration – Enterprise AI is rarely a standalone application. You will be evaluated on your ability to integrate LLMs into existing enterprise software stacks, manage data pipelines, handle API rate limits, and design systems that are secure, scalable, and cost-effective.

Problem-Solving & Adaptability – Because the GenAI space changes weekly, interviewers value candidates who can think from first principles. When presented with an unfamiliar scenario or a system failure (such as hallucination or retrieval drift), you need to demonstrate a structured, logical debugging methodology.

Communication & Client Readiness – As an Infosys engineer, you may interact directly with clients or internal business stakeholders. You must be able to translate complex technical AI concepts into clear, business-oriented outcomes, demonstrating how your technical decisions drive value.

The hiring process for a GenAI Engineer at Infosys is structured to thoroughly evaluate both your technical depth and your alignment with the company's consulting and engineering culture. While the process is highly standardized, the depth of technical questioning scales significantly with the seniority of the role.

For experienced candidates, the journey typically begins with an initial screening by an offshore or local recruiter to verify your background and align on role expectations. This is followed by one or two deep-dive technical rounds conducted virtually via Microsoft Teams. These rounds focus heavily on live coding, system design, and architectural discussions around generative AI. If you pass these, you will move to a managerial round that combines complex problem-solving with behavioral scenarios, followed by a final HR round to discuss compensation and logistics.

The timeline above outlines the typical progression from initial contact to the final decision. Candidates should interpret this as a guide to pace their preparation, focusing first on core technical concepts and coding, before shifting focus to system architecture and behavioral scenarios as they advance. While the process generally moves efficiently, staying in close contact with your recruiter is key to navigating the transitions between stages.

To succeed in the technical rounds, you must demonstrate mastery across several core domains. Infosys interviewers will evaluate your practical engineering experience rather than just theoretical knowledge.

Large Language Models & Tokenization

Understanding the underlying mechanics of LLMs is critical for optimizing performance and cost in enterprise environments.

You must be prepared to discuss how text is transformed into a format that models can process and how this affects overall system behavior.

Be ready to go over:

• Tokenization Mechanics – How Byte-Pair Encoding (BPE) or WordPiece algorithms segment text, and why certain characters or languages consume more tokens.
• Context Window Management – Strategies for handling long inputs, including truncation, sliding windows, and prompt compression techniques.
• Model Selection & Trade-offs – When to utilize proprietary models (like GPT-4) versus hosting open-source alternatives (like LLaMA or Mistral) based on latency, privacy, and cost.
• Advanced concepts (less common) – Fine-tuning methodologies (such as LoRA and QLoRA), understanding KV-caching, and optimizing inference speeds using quantization (e.g., AWQ, GPTQ).

Example questions or scenarios:

• "How would you optimize a prompt template to minimize token usage while maintaining high accuracy for a classification task?"
• "Explain how a tokenizer's handling of special characters can affect the performance of an LLM designed to parse code or structured JSON."

Retrieval-Augmented Generation (RAG) & Chunking

Since most enterprise data is proprietary, building high-quality RAG pipelines is one of the most common tasks for a GenAI Engineer at Infosys.

You must demonstrate a comprehensive understanding of how to ingest, index, retrieve, and synthesize information effectively.

Be ready to go over:

• Chunking Strategies – The pros and cons of fixed-character chunking, overlapping chunks, document-based chunking, and semantic chunking based on sentence similarity.
• Embedding Models – How to select and evaluate embedding models for specific domains (e.g., financial, legal, or medical text).
• Vector Search & Indexing – Understanding hierarchical navigable small world (HNSW) graphs, inverted file index (IVF), and how to scale vector search in production.
• Advanced concepts (less common) – Implementing self-RAG, query rewriting, metadata filtering, and utilizing cross-encoder re-ranking models to improve precision.

Example questions or scenarios:

• "Walk me through how you would design a RAG system for a client with millions of multi-page PDF documents containing complex tables and images."
• "If your RAG system is consistently retrieving irrelevant information, what debugging steps would you take to isolate the issue?"

Agentic AI & Orchestration

Modern AI systems are transitioning from static pipelines to dynamic, autonomous agents that can plan, use tools, and collaborate.

Interviewers will evaluate your ability to design systems that can reason and execute multi-step tasks.

Be ready to go over:

• Tool-Calling & Function Binding – How to define schemas that allow an LLM to reliably generate arguments for external API calls.
• State and Memory Management – How to maintain short-term and long-term memory across complex, multi-turn conversations and agent loops.
• Framework Proficiency – Your practical experience with orchestration libraries such as LangChain, LangGraph, LlamaIndex, or CrewAI.
• Advanced concepts (less common) – Implementing the Model Context Protocol (MCP), managing multi-agent negotiation, and preventing infinite loops in autonomous execution.

Example questions or scenarios:

• "Design an agentic system that can take a user's natural language request, query a SQL database, format the result into a CSV, and email it to a stakeholder."
• "How do you ensure that an AI agent does not execute harmful or unintended tool calls when processing untrusted user input?"

As a GenAI Engineer at Infosys, your day-to-day work will be highly collaborative, bridging the gap between cutting-edge AI research and robust software engineering.

• Architecting AI Solutions – You will design and implement end-to-end generative AI applications, ensuring they meet enterprise standards for scalability, security, and latency. This includes setting up vector databases, configuring LLM APIs, and establishing monitoring pipelines.
• Optimizing Performance and Cost – Enterprise deployments require strict budget and latency management. You will be responsible for prompt engineering, implementing caching layers (such as GPTCache), selecting cost-effective model routing, and fine-tuning models when necessary.
• Collaborating Across Teams – You will work closely with Data Engineers to build robust ETL pipelines for ingestion, Cloud Architects to deploy models securely, and Product Managers to translate business requirements into technical AI workflows.
• Client-Facing Consulting – You will frequently assist in translating complex AI capabilities into practical solutions for clients, helping them understand what is feasible, what is experimental, and how to safely adopt AI technologies.

To be competitive for this role at Infosys, you need a strong foundation in software engineering coupled with specialized expertise in machine learning and natural language processing.

• Technical Skills – Strong proficiency in Python is mandatory. You should have hands-on experience with deep learning frameworks (PyTorch or TensorFlow), LLM orchestration tools (LangChain, LlamaIndex, LangGraph), vector databases (Milvus, Pinecone, Qdrant, Chroma), and cloud platforms (AWS, Azure, or GCP).
• Experience Level – Typically, Infosys looks for candidates with 3+ years of experience in data science, machine learning, or software engineering, with at least 1–2 years of dedicated experience building and deploying generative AI systems in production.
• Soft Skills – Excellent communication skills are essential. You must be comfortable presenting technical architectures to non-technical stakeholders and navigating the ambiguity of rapidly changing project requirements.

Must-have skills:

• Advanced Python programming and software design patterns.
• Practical experience building and deploying production-grade RAG pipelines.
• Deep understanding of transformer architectures, embeddings, and vector databases.

Nice-to-have skills:

• Experience fine-tuning open-source LLMs using PEFT/LoRA.
• Familiarity with containerization (Docker, Kubernetes) and MLOps pipelines.
• Knowledge of agentic frameworks and multi-agent system design.

Q: Is there a coding round in the GenAI Engineer interview? Yes. Even for senior roles, Infosys typically expects candidates to demonstrate hands-on coding proficiency in Python. Be prepared for live coding exercises that involve data manipulation, API integration, or implementing basic AI algorithms.

Q: How much preparation time is recommended? For a candidate with prior machine learning experience, 2 to 3 weeks of focused preparation is usually sufficient. This time should be spent reviewing transformer mechanics, practicing RAG system design, and building a small agentic project to solidify your understanding of modern frameworks.

Q: How does Infosys view remote or hybrid work for this role? Infosys generally operates on a hybrid model, requiring engineers to spend a designated number of days per week in a local development center (such as Bengaluru or Mississauga), depending on client and project requirements.

Q: What is the average timeline from the first HR call to an offer? The entire process typically takes between 3 to 5 weeks, depending on interviewer availability and the urgency of the hiring business unit.

To truly stand out during your Infosys interview, keep these practical, insider tips in mind:

• Clarify Coding Expectations Early:

• Prepare for Philosophical AI Questions: Do not dismiss questions like "Will AI take our jobs?" as simple icebreakers. Use them to demonstrate your industry maturity, your pragmatic understanding of AI limitations, and your vision for how humans and AI should collaborate in an enterprise environment.
• Be Ready to Discuss Failures: Interviewers love to hear about what went wrong in your previous projects. Prepare stories about how you debugged a hallucinating model, fixed a bottleneck in a vector search, or optimized an incredibly slow RAG pipeline.
• Focus on Business Metrics: When describing your projects, do not just mention accuracy or F1 scores. Explain how your GenAI solution reduced operational costs, improved customer satisfaction scores, or accelerated data retrieval times for the business.

A GenAI Engineer position at Infosys is an exceptional opportunity to work on cutting-edge generative AI applications at an unprecedented global scale. By driving projects within the Infosys Topaz ecosystem, you will actively shape how major enterprises navigate the transition into an AI-driven future.

To maximize your chances of success, focus your preparation on the core mechanics of RAG pipelines, the design of agentic workflows, and practical Python coding. Be ready to articulate not just the "how" of your technical implementations, but also the "why"—demonstrating a sharp eye for cost, latency, security, and client-facing business value.

The compensation data reflects the competitive nature of specialized AI roles at Infosys. When preparing your salary expectations, consider how your specific expertise in high-demand areas—such as custom agentic workflows or enterprise RAG optimization—aligns with the value you will deliver to the organization's global clients.

If you are ready to take the next step and dive deeper into real-world interview experiences, detailed question banks, and community insights, explore the comprehensive resources available on Dataford to ensure you are fully prepared to ace your upcoming interviews.