New opportunity

Senior AI Engineer

A Large Global Organization · Bengaluru, India

About this role

The Senior AI Engineer will design and develop Generative AI applications using large language models, prompt patterns, and structured response handling. The role involves building Retrieval-Augmented Generation (RAG) pipelines to ensure grounded responses, integrating AI capabilities with enterprise data sources, and implementing evaluation, relevance, and safety checks for model outputs. Responsibilities include optimizing workflows for latency, cost, and reliability in production cloud environments. Required qualifications include 7-10 years of experience in AI, Machine Learning, or software engineering, with substantial hands-on experience in Generative AI and LLMs. Candidates must demonstrate practical experience in prompt engineering, RAG solution development, and Azure AI services integration. A strong understanding of model evaluation, performance optimization, and cloud deployment is essential. A graduate degree in any discipline is required. Preferred qualifications include experience with Vector Database Integration, Databricks, or Amazon Bedrock.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Prompt EngineeringGenerative AIAzure AIRetrieval-Augmented Generation (RAG)Vector Database IntegrationAmazon BedrockDatabricksMachine LearningLarge Language Models (LLMs)Cloud Deployment

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

RAG Pipeline Development and Grounding
The core requirement is to build Retrieval-Augmented Generation pipelines that connect enterprise data to LLMs. You must demonstrate how to ensure model responses are grounded in retrieved context, minimizing hallucinations while maintaining relevance to specific business data sources.
Production-Grade AI Optimization
Beyond development, the role demands optimizing workflows for latency, cost, and reliability. You must be prepared to discuss how you balance model performance with operational constraints in a production cloud environment, specifically using Azure AI services.
Evaluation and Safety Frameworks
Implementing robust evaluation, relevance, and safety checks is a key responsibility. You should be ready to explain your methodology for measuring the quality of AI outputs and the technical implementation of guardrails to ensure enterprise-grade reliability.

Likely challenges to think through

Balancing Model Performance with Enterprise Constraints
A significant challenge is the trade-off between model complexity and production requirements like latency and cost. Prepare by reviewing strategies for model selection, caching, and efficient retrieval techniques that maintain high quality without exceeding budget or response time limits.
Integrating Diverse Enterprise Data Sources
Integrating AI with existing enterprise data often involves complex data pipelines and security considerations. Be prepared to discuss how you handle data ingestion, indexing in vector databases, and the synchronization required to keep RAG context accurate and secure.

Practice your answers

Technical practice

Designing RAG Pipelines for Grounded Responses

What to coverExplain the architecture of a Retrieval-Augmented Generation pipeline. Focus on how you handle document chunking, embedding strategies, and the retrieval process to minimize hallucinations and ensure the model remains grounded in enterprise data.

Prompt Engineering Strategies for Structured Outputs

What to coverDescribe your approach to designing prompt patterns that enforce structured response formats, such as JSON. Discuss how you manage context window constraints and system instructions to maintain consistency across complex interactions.

Evaluating LLM Output Quality and Safety

What to coverWhat metrics and frameworks do you use to evaluate the relevance, accuracy, and safety of LLM-generated content? Explain how you implement automated guardrails to detect and mitigate harmful or biased outputs in a production environment.

Optimizing AI Workflows for Latency and Cost

What to coverDiscuss techniques for optimizing LLM inference, such as caching strategies, model quantization, or choosing appropriate model sizes for specific tasks. How do you balance the trade-off between model performance and operational costs in a cloud-based production system?

Integrating Enterprise Data with Azure AI

What to coverDescribe your experience integrating Azure AI services with internal enterprise data sources. Address how you handle data security, access control, and synchronization between your vector database and the primary data store.

Vector Database Selection and Integration

What to coverCompare different vector database solutions and explain how you determine the best fit for a specific use case. Discuss the challenges of maintaining index freshness and query performance as the volume of enterprise data scales.

Cloud Deployment and Reliability

What to coverWhat are the key considerations when deploying Generative AI applications to a production cloud environment? Focus on monitoring, logging, and building resilience into the pipeline to ensure high availability and reliability.

Leveraging Databricks or Amazon Bedrock

What to coverIf you have experience with Databricks or Amazon Bedrock, explain how these platforms facilitate the development or deployment of LLM applications. Highlight specific features that simplify the lifecycle management of AI models.

Behavioural practice

Handling Technical Ambiguity in AI Projects

What to coverDescribe a situation where you had to design an AI solution with unclear requirements or evolving technology. Explain how you defined the scope, managed stakeholder expectations, and ensured the final output met business needs.

Managing Trade-offs in Production Systems

What to coverGive an example of a time you had to make a difficult trade-off between model accuracy, latency, and cost. How did you evaluate the options, and what was the final impact on the product's performance?

Collaborating Across Technical Teams

What to coverDescribe a project where you integrated AI capabilities into an existing enterprise workflow. How did you coordinate with data engineers, software developers, or security teams to ensure a successful and secure deployment?

Learning and Adapting to New AI Advancements

What to coverThe field of Generative AI evolves rapidly. Provide an example of how you identified a new tool or technique, evaluated its potential value for your project, and successfully implemented it to improve your workflow.

Sharpen your knowledge

Topics to prepare

Retrieval-Augmented Generation (RAG) Architecture
Review the conceptual framework of RAG, focusing on how to ground LLM responses using external enterprise data. Exercise: Design a high-level architecture diagram for a RAG pipeline that includes document ingestion, vectorization, retrieval strategies, and output verification to ensure relevance and safety.
AI Model Evaluation and Safety
Study methodologies for evaluating LLM outputs, including metrics for relevance, grounding, and safety. Exercise: Define a testing strategy for a generative application that identifies potential hallucinations and implements guardrails to maintain response reliability in a production environment.

Skills to revise

Prompt Engineering Strategies
Develop advanced prompt patterns for structured response handling. Exercise: Create a set of system prompts and few-shot examples designed to force an LLM to output data in a specific JSON schema while maintaining strict adherence to provided context.
Azure AI Services Integration
Gain hands-on familiarity with deploying and managing AI models within the Azure ecosystem. Exercise: Practice configuring an Azure AI service endpoint and integrating it into a workflow that manages latency and cost-effective API calls.
Vector Database Integration
Practice connecting LLM applications to vector stores for efficient semantic search. Exercise: Implement a basic integration between a document store and a vector database, focusing on how to perform similarity searches that feed into the context window of an LLM.

Your preparation checklist

Architect a RAG pipeline walkthrough
Prepare a technical overview of a RAG system you have built, focusing on how you handled data ingestion, vector database integration, and retrieval accuracy. Be ready to explain your choice of embedding models and how you ensured grounded, relevant responses.
Demonstrate prompt engineering and output control
Prepare examples of complex prompt patterns you have designed to enforce structured outputs (e.g., JSON) and safety guardrails. Explain how you tested these prompts for consistency and reliability in a production environment.
Prepare for cloud-native AI optimization discussions
Review your experience with Azure AI or Amazon Bedrock in the context of production deployment. Be prepared to discuss specific strategies you used to optimize for latency, cost-efficiency, and system reliability at scale.
Develop a framework for model evaluation
Outline your methodology for evaluating LLM performance, including metrics for relevance, safety, and hallucination detection. Explain how you integrate these evaluation checks into an automated CI/CD pipeline.
Formulate strategic questions for the interviewer
Prepare questions regarding the organization's current enterprise data integration challenges, the specific cloud infrastructure (Azure vs. AWS) they prioritize, and how they balance model performance with production cost constraints.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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