New opportunity

Lead Software Engineer

Virtusa · Mumbai, India

About this role

The Lead Software Engineer will be responsible for designing and developing scalable backend services and AI integrations. The role focuses on building enterprise-grade LLM-driven systems and agentic workflows. Responsibilities: Design and develop backend services using .NET and ASP.NET Core. Implement memory management, tool-calling, and structured context handling for LLM systems. Build and optimize RAG pipelines using vector databases and embeddings. Orchestrate agentic workflows using frameworks like Semantic Kernel, LangChain, LlamaIndex, or AutoGen. Apply advanced prompt engineering techniques such as ReAct, Chain-of-Thought, and function-calling. Integrate LLM APIs including Azure OpenAI and Gemini. Develop secure APIs with OAuth2/JWT and manage cloud-native deployments on Microsoft Azure. Containerize applications using Docker and AKS. Ensure performance optimization, monitoring, and CI/CD implementation via Azure DevOps. Requirements: 7-10 years of professional experience. Proficiency in .NET, C#, and cloud-native development. Experience with vector databases, semantic retrieval, and AI security/governance standards.

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

Skills for this role

.NETC#ASP.NET CoreLLMRAGSemantic KernelLangChainLlamaIndexAutoGenPrompt EngineeringAzure OpenAIGeminiRESTWebSocketsOAuth2JWTMicrosoft AzureDockerAzure Kubernetes ServiceAzure Cognitive SearchAzure DevOps

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

Enterprise LLM Integration
The core of this role involves building production-ready systems that integrate LLMs like Azure OpenAI and Gemini. You must demonstrate expertise in managing the full lifecycle of these integrations, including tool-calling, memory management, and ensuring structured context handling within .NET backend services.
RAG Pipeline Optimization
You are expected to design and refine Retrieval-Augmented Generation pipelines. This requires deep knowledge of vector databases, embedding strategies, and semantic retrieval techniques using tools like Azure Cognitive Search to ensure high accuracy and relevance in AI-generated outputs.
Agentic Workflow Orchestration
A key priority is the development of autonomous agentic workflows. You must be proficient in orchestrating these systems using frameworks such as Semantic Kernel, LangChain, or AutoGen, while applying advanced prompt engineering techniques like ReAct and Chain-of-Thought.
Cloud-Native Backend Engineering
The role requires building scalable, secure, and high-performance backend services using .NET and ASP.NET Core. You must be capable of deploying these services on Microsoft Azure using Docker and AKS, while maintaining strict security standards via OAuth2/JWT and robust CI/CD practices.

Likely challenges to think through

Balancing AI Non-Determinism with Enterprise Reliability
Integrating LLMs into enterprise systems introduces non-deterministic behavior. Prepare to discuss how you implement guardrails, structured outputs, and rigorous monitoring to ensure that AI-driven features remain reliable, secure, and compliant with governance standards.
Managing Latency in Complex Agentic Workflows
Orchestrating multi-step agentic workflows can lead to significant latency issues. Be ready to explain your approach to performance optimization, such as asynchronous processing, effective caching strategies, and efficient tool-calling to maintain a responsive user experience.
Evolving AI Security and Governance
As AI security standards are rapidly evolving, you will face the challenge of securing data privacy within LLM interactions. Prepare to discuss how you handle sensitive data in RAG pipelines and ensure that your API integrations adhere to enterprise-grade security and compliance requirements.

Practice your answers

Technical practice

Designing Scalable RAG Pipelines

What to coverExplain your approach to building a RAG pipeline, specifically how you handle embedding generation, vector database selection, and semantic retrieval optimization. Discuss how you manage context window limitations and ensure the accuracy of retrieved information.

Agentic Workflow Orchestration

What to coverCompare the use of frameworks like Semantic Kernel, LangChain, or AutoGen for building agentic workflows. Describe how you implement tool-calling and memory management to allow agents to perform multi-step tasks effectively.

Advanced Prompt Engineering Techniques

What to coverDiscuss the practical application of ReAct and Chain-of-Thought prompting in enterprise systems. Explain how you ensure structured outputs from LLMs and maintain reliability when integrating these models into production APIs.

Performance Optimization in .NET

What to coverHow do you leverage asynchronous programming, parallel processing, and streaming within ASP.NET Core to ensure high-performance backend services? Provide examples of how you identify and resolve bottlenecks in AI-integrated applications.

Cloud-Native Deployment and Orchestration

What to coverDescribe your experience containerizing .NET applications with Docker and managing them on Azure Kubernetes Service (AKS). Explain your strategy for scaling these services and managing configurations in a cloud-native environment.

AI Security and Governance

What to coverWhat strategies do you employ to ensure compliance with data privacy and security standards when integrating LLMs? Address how you handle sensitive data, API authentication (OAuth2/JWT), and potential prompt injection vulnerabilities.

Monitoring and Troubleshooting AI Services

What to coverHow do you utilize Azure Monitor and Application Insights to track the performance and health of LLM-driven systems? Explain your process for logging and debugging issues that arise from non-deterministic AI model responses.

CI/CD Implementation with Azure DevOps

What to coverOutline your approach to setting up CI/CD pipelines for AI-integrated applications. Discuss how you manage automated testing for LLM components and ensure seamless deployments to Azure environments.

Behavioural practice

Leading Technical Design Decisions

What to coverDescribe a situation where you had to lead the design of a complex system. How did you evaluate trade-offs between different technologies or architectural patterns, and how did you gain consensus from your team?

Handling Ambiguity in AI Projects

What to coverShare an example of a project where the requirements for an AI integration were unclear or evolving. How did you manage the uncertainty, and what steps did you take to deliver a functional, enterprise-grade solution?

Mentorship and Technical Guidance

What to coverAs a Lead Engineer, how do you foster technical growth within your team? Provide an example of how you mentored a team member through a difficult technical challenge or helped them adopt new AI-related technologies.

Managing Stakeholder Expectations

What to coverTell me about a time you had to explain a complex technical limitation of an AI system to non-technical stakeholders. How did you communicate the trade-offs and ensure the project goals remained aligned with business needs?

Sharpen your knowledge

Topics to prepare

Agentic Workflow Orchestration
Focus on the conceptual design of autonomous systems that use tool-calling and memory management. Practice mapping out how an agent decides which tool to invoke based on user input, which is essential for the role's requirement to build LLM-driven systems using frameworks like Semantic Kernel or AutoGen.
RAG Pipeline Architecture
Review the end-to-end flow of Retrieval-Augmented Generation, specifically focusing on embedding strategies, vector database indexing, and semantic retrieval optimization. This is critical for the role's focus on building scalable AI integrations that maintain high accuracy and relevance.
AI Security and Governance
Study enterprise standards for AI data privacy, prompt injection mitigation, and secure API integration. This aligns with the requirement to ensure compliance with security and governance standards when deploying LLM-based applications in a corporate environment.

Skills to revise

Advanced .NET Backend Development
Practice implementing high-performance asynchronous patterns, parallel processing, and streaming within ASP.NET Core. This is necessary to meet the requirement for building scalable backend services that handle complex AI workloads.
Azure Cloud-Native Deployment
Exercise containerizing .NET applications with Docker and deploying them to Azure Kubernetes Service (AKS). Focus on configuring CI/CD pipelines in Azure DevOps to ensure automated, secure, and monitorable deployments.
Prompt Engineering Techniques
Develop hands-on experience with structured outputs, ReAct, and Chain-of-Thought prompting. Practice these techniques to improve the reliability of LLM responses, as this is a core technical requirement for the role.

Your preparation checklist

Architect a RAG pipeline walkthrough
Prepare a technical overview of a RAG system you have built or designed. Focus on your choices for vector databases, embedding models, and how you handled context window limitations and semantic retrieval accuracy.
Demonstrate agentic workflow expertise
Be ready to discuss your experience with frameworks like Semantic Kernel or LangChain. Prepare to explain how you implemented tool-calling, memory management, and ReAct or Chain-of-Thought prompting in a production environment.
Review .NET and Azure cloud-native patterns
Refresh your knowledge on building scalable ASP.NET Core services, specifically focusing on asynchronous programming, OAuth2/JWT security, and deploying containerized applications via Docker and AKS on Microsoft Azure.
Prepare AI governance and security talking points
Formulate a perspective on enterprise AI security, including data privacy, prompt injection mitigation, and how you monitor LLM-driven systems using Azure Monitor and Application Insights.
Formulate strategic questions for the team
Prepare questions regarding the current maturity of their AI integrations, the specific LLM models they prioritize (e.g., Azure OpenAI vs. Gemini), and how they balance rapid AI experimentation with enterprise-grade CI/CD requirements in Azure DevOps.

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