New opportunity

Assistant Manager - Applied AI

IT Services Organization · Hyderabad, India

About this role

The Assistant Manager - Applied AI will be responsible for designing and developing scalable backend services and AI-enabled applications. This role focuses on building, integrating, and deploying AI agent capabilities within enterprise architectures using Azure Cloud services. Responsibilities include developing secure APIs, supporting Generative AI use cases, and collaborating with cross-functional teams to deliver production-ready implementations. The candidate will also provide technical leadership, conduct code reviews, and ensure high-quality delivery throughout the software development lifecycle. Required Qualifications: 6-9 years of relevant software engineering and applied AI experience; strong proficiency in C#, .NET, and .NET Core; practical experience with Azure Cloud; proven experience in developing AI agents or integrating agent-based capabilities; and successful delivery of at least one Generative AI project involving LLM, RAG, LangChain, or prompt engineering. A Bachelor's degree in Computer Science, IT, Engineering, or a related field is required.

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

Skills for this role

C#C#.net.NET.NET CoreAzure CloudAgentic AILLMLangChainPrompt EngineeringGenerative AIRAG

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

Integration of AI Agents in Enterprise Architectures
The role requires building and deploying AI agent capabilities specifically within existing enterprise frameworks on Azure. Success depends on balancing advanced AI functionality with the stability and security requirements of enterprise-grade backend services.
Full-Stack .NET Development for AI Applications
Candidates must demonstrate strong proficiency in C#, .NET, and .NET Core to design scalable backend services that support AI-enabled features. This is a core requirement for delivering production-ready implementations.
Technical Leadership and Quality Assurance
As an Assistant Manager, the individual is expected to provide technical guidance, conduct code reviews, and ensure high-quality delivery throughout the software development lifecycle, necessitating a balance between hands-on coding and team oversight.

Likely challenges to think through

Bridging Traditional .NET Backend with Generative AI
Integrating modern LLM, RAG, and LangChain workflows into established .NET backend architectures can introduce latency and complexity. Prepare by discussing how you manage asynchronous processing, API security, and state management when dealing with non-deterministic AI outputs.
Ensuring Production-Grade Reliability for AI Agents
Moving from experimental AI prototypes to production-ready implementations requires rigorous testing and error handling. Prepare by highlighting your experience with monitoring, logging, and fallback mechanisms for agentic workflows in a cloud-native environment.

Practice your answers

Technical practice

Architecting Scalable AI-Enabled Backend Services in .NET

What to coverExplain how you would design a scalable backend service using .NET Core to handle high-concurrency requests for an AI-agent application. Focus on architectural patterns, such as asynchronous processing or microservices, and how you ensure the system remains responsive during intensive LLM inference tasks.

Implementing RAG Architectures on Azure

What to coverDescribe your approach to building a Retrieval-Augmented Generation (RAG) pipeline within the Azure ecosystem. Discuss your choice of vector storage, how you manage document ingestion, and the strategies used to ensure data security and retrieval accuracy.

Agentic AI Orchestration with LangChain

What to coverDiscuss the challenges of building autonomous AI agents using LangChain. Explain how you manage agent state, tool selection, and error handling when an agent fails to complete a multi-step task.

Optimizing Prompt Engineering for Enterprise Use Cases

What to coverHow do you approach prompt engineering to ensure consistent, high-quality outputs from LLMs in a production environment? Explain your methodology for testing, versioning, and mitigating hallucinations in business-critical applications.

Securing AI-Integrated APIs

What to coverWhat security considerations are paramount when exposing AI-enabled features through APIs? Address topics such as input validation to prevent prompt injection, authentication, and managing sensitive data exposure in LLM interactions.

Azure Cloud Services for AI Deployment

What to coverWhich Azure services would you leverage to deploy and monitor a production-grade Generative AI application? Explain your reasoning for selecting specific services for compute, model hosting, and observability.

Code Review Standards for AI Projects

What to coverAs an Assistant Manager, what specific criteria do you prioritize when conducting code reviews for AI-integrated .NET applications? Focus on maintainability, performance, and the integration of non-deterministic AI components.

Managing the AI Software Development Lifecycle

What to coverHow does the development lifecycle for an AI-enabled project differ from traditional software engineering? Suggest how you integrate testing, evaluation, and CI/CD pipelines specifically for LLM-based features.

Behavioural practice

Technical Leadership and Mentorship

What to coverDescribe a situation where you had to provide technical guidance to a team member struggling with a complex AI integration. How did you balance providing support with ensuring the project met its delivery timeline?

Collaborating with Cross-Functional Teams

What to coverGive an example of a time you collaborated with product managers or stakeholders to define the scope of an AI use case. How did you manage expectations regarding the capabilities and limitations of the technology?

Handling Project Setbacks

What to coverTell me about a time when a production-ready AI implementation failed to meet performance or quality requirements. What steps did you take to diagnose the issue and what was the final outcome?

Balancing Innovation with Production Stability

What to coverDescribe a scenario where you had to choose between implementing a cutting-edge AI technique and a more stable, proven solution. How did you justify your decision to the team and stakeholders?

Sharpen your knowledge

Topics to prepare

RAG Architecture and Implementation
Review the conceptual flow of Retrieval-Augmented Generation, specifically how to connect external data sources to LLMs. Exercise: Design a high-level architecture diagram for a document-based Q&A system, detailing the indexing, retrieval, and generation phases. This is directly relevant to the requirement for delivering production-ready Generative AI projects.
Agentic AI Design Patterns
Study the principles of autonomous agents, including planning, tool use, and memory management. Exercise: Outline how an AI agent would handle a multi-step task, such as querying a database and summarizing the result for a user. This aligns with the role's focus on building and integrating agent-based capabilities.

Skills to revise

LangChain Development
Practice building chains and agents using the LangChain framework. Exercise: Create a simple C# or Python script that uses LangChain to chain a prompt template with an LLM call. This is a core technical requirement for the role's focus on Generative AI integration.
Azure Cloud AI Services
Familiarize yourself with deploying AI models on Azure. Exercise: Explore the documentation for Azure OpenAI Service and practice setting up a secure API endpoint. This is essential for the role's responsibility to deploy AI-enabled applications within enterprise architectures.
Advanced .NET Core API Development
Focus on building secure, scalable backend services. Exercise: Develop a secure RESTful API in .NET Core that implements authentication and integrates with an external AI service. This addresses the requirement for developing secure APIs and scalable backend services.
Prompt Engineering Techniques
Refine methods for optimizing LLM outputs. Exercise: Take a complex business requirement and draft a structured system prompt, then test it against an LLM to evaluate consistency and accuracy. This is explicitly listed as a key skill for the position.

Your preparation checklist

Prepare a Generative AI project walkthrough
Document a specific project where you implemented an LLM, RAG, or LangChain solution. Be prepared to explain your architectural choices, how you handled data retrieval, and the specific prompt engineering techniques used to improve output quality.
Review .NET Core and Azure integration patterns
Refresh your knowledge on building secure, scalable backend APIs using C# and .NET Core. Focus on how these services integrate with Azure Cloud services, specifically for hosting AI models or managing agentic workflows.
Develop an agentic AI design scenario
Create a high-level design for an AI agent that automates a business process. Be ready to discuss how you would handle agent orchestration, tool selection, and error handling within an enterprise architecture.
Formulate technical leadership questions
Prepare questions for the interviewer regarding their current AI development lifecycle, such as how they manage code reviews for AI-integrated applications or how they balance rapid prototyping with production-ready stability.

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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