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Senior Software Engineer

Microsoft · Bangalore, India

About this role

The Perception team at Microsoft is seeking a Senior Software Engineer to contribute to the Perception platform, an advanced innovation environment focused on agentic development within Microsoft Security. This role involves building and implementing AI agentic and platform components, including Agents and Model Context Protocols (MCPs) for MSEC products. Responsibilities include contributing to high-level and low-level design, utilizing AI to structure and write complex platform code, and managing end-to-end software engineering tasks such as development, deployment, testing, and maintenance. Candidates must demonstrate a passion for solving complex platform challenges, particularly in agent development, and possess the ability to scale and deliver aggressively. Required Qualifications: Candidates must hold a BTECH/BE or equivalent degree and have at least 8 years of relevant experience. Essential skills include deep expertise in distributed systems, microservices, data storage, and service reliability, along with strong backend programming proficiency in Java, Python, or .Net. Applicants should have experience operating services at enterprise scale with solid SLO requirements, a deep understanding of APIs, service contracts, authentication, authorization, and security principles, and a proven track record of leading cross-team technical initiatives and designing cloud-native architectures. Preferred Qualifications: Experience with AI platforms, LLM-based systems, agents, orchestration frameworks, or developer platforms is highly desirable. Additional Requirements: The role requires the ability to meet Microsoft, customer, and government security screening requirements, including passing a Microsoft Cloud Background Check upon hire and every two years thereafter. The position is based in Bangalore, India, with a hybrid work arrangement requiring 3 days per week in-office.

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

Skills for this role

AI AgentsMulti-agent workflowsDistributed systemsMicroservicesJavaPython.NetCloud-native architectureLLM-based systemsOrchestration frameworksAPI designService reliability

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

Development of AI Agentic Platforms
The core mandate is building and implementing AI agents and Model Context Protocols (MCPs) for Microsoft Security products. You must demonstrate the ability to integrate these components into a high-scale platform while maintaining security and performance standards.
Enterprise-Scale Distributed Systems Engineering
The role requires managing end-to-end software lifecycles for services at enterprise scale. You must prioritize service reliability, strict SLO adherence, and cloud-native architecture design, ensuring that complex platform code remains maintainable and robust.
Cross-Team Technical Leadership
As a Senior Software Engineer, you are expected to drive architectural decisions and lead technical initiatives that span multiple engineering organizations. This involves influencing product direction and aligning disparate teams on service contracts and security principles.

Likely challenges to think through

Balancing AI Innovation with Security Constraints
The challenge lies in using AI to accelerate code generation and platform development without compromising the stringent security requirements inherent to Microsoft Security products. Prepare by articulating how you maintain rigorous security audits and quality gates while leveraging AI-assisted development workflows.
Orchestrating Complex Multi-Agent Workflows
Designing and scaling agentic systems involves managing non-deterministic behaviors and complex orchestration frameworks. Prepare by discussing your experience with state management, observability in LLM-based systems, and how you handle failure modes in distributed agent environments.

Practice your answers

Technical practice

Designing for Agentic Workflows

What to coverExplain how you would architect a multi-agent system to ensure reliability and state management. Discuss the trade-offs between synchronous and asynchronous communication patterns when orchestrating complex agentic tasks.

Implementing Model Context Protocols (MCPs)

What to coverDescribe your approach to integrating MCPs within a secure platform. Focus on how you maintain consistent service contracts and data security while allowing LLMs to interact with external tools and data sources.

Scaling Distributed Systems at Enterprise Level

What to coverDiscuss strategies for maintaining high availability and meeting strict SLOs in a cloud-native environment. Address how you handle service discovery, load balancing, and failure recovery in a microservices architecture.

Security in AI-Driven Platforms

What to coverHow do you integrate authentication and authorization into an agentic platform? Explain how you protect against prompt injection or unauthorized data access when agents interact with sensitive MSEC product components.

Leveraging AI for Code Generation

What to coverThe role involves using AI to structure and write complex platform code. Discuss your methodology for validating AI-generated code to ensure it meets enterprise standards for security, performance, and maintainability.

Service Reliability and Observability

What to coverWhat metrics and observability tools do you prioritize when managing services at scale? Explain how you debug issues in a distributed system where multiple agents might be interacting across different service boundaries.

Cloud-Native Architecture Trade-offs

What to coverCompare different data storage strategies for AI platforms, such as vector databases versus traditional relational databases. Explain when you would choose one over the other based on latency, consistency, and scalability requirements.

Orchestration Frameworks

What to coverEvaluate the role of orchestration frameworks in managing LLM-based systems. Discuss how you handle long-running processes and state persistence when an agent requires multiple steps to complete a user request.

Behavioural practice

Leading Cross-Team Initiatives

What to coverDescribe a time you led a technical initiative that required alignment across multiple engineering teams. Focus on how you managed conflicting priorities and ensured the final architecture met the needs of all stakeholders.

Navigating Ambiguity in Innovation

What to coverProvide an example of a project where you had to innovate in an emerging technology space, such as agentic development. How did you define success and manage the technical risks involved?

Driving Architectural Decisions

What to coverTell me about a time you had to advocate for a specific architectural decision that was met with resistance. How did you use data and technical evidence to influence the product direction?

Balancing Velocity and Quality

What to coverDescribe a situation where you had to deliver a complex feature under tight deadlines. How did you ensure the code remained secure and reliable without compromising the delivery schedule?

Sharpen your knowledge

Topics to prepare

Agentic AI and Model Context Protocols (MCPs)
Review the conceptual framework of agentic systems, specifically how agents interact with external tools and data via MCPs. Exercise: Draft a high-level design document for an agentic workflow that uses an MCP to retrieve security telemetry, focusing on how the agent maintains context and security boundaries.
Cloud-Native Distributed Systems Reliability
Focus on designing for high availability and strict Service Level Objectives (SLOs) in a microservices environment. Exercise: Analyze a hypothetical failure scenario in a distributed system and outline a strategy for implementing circuit breakers, retries, and observability to maintain SLOs under load.
Security Principles in Platform Engineering
Deepen understanding of authentication, authorization, and secure service contracts in enterprise-scale systems. Exercise: Review common security vulnerabilities in microservice communication and propose a design for a secure, authenticated service-to-service contract.

Skills to revise

Backend Programming (Java, Python, or .Net)
Demonstrate proficiency in building scalable backend services. Exercise: Refactor a legacy monolithic function into a clean, testable, and modular service component using your primary language, ensuring it adheres to modern cloud-native design patterns.
Orchestration Frameworks for LLMs
Gain hands-on experience with tools used to manage LLM-based workflows. Exercise: Build a simple prototype using an orchestration framework to chain multiple LLM calls, demonstrating how to manage state and handle errors between agentic steps.
Cross-Team Technical Leadership
Practice the ability to drive architectural decisions across organizational boundaries. Exercise: Prepare a case study from your past experience detailing how you led a technical initiative, specifically highlighting how you aligned stakeholders, managed trade-offs, and communicated the architectural vision.

Your preparation checklist

Map distributed systems experience to agentic platforms
Prepare a technical narrative detailing how you have designed and scaled microservices at an enterprise level. Specifically, connect your experience with service reliability, SLO management, and API contracts to the requirements of building AI agentic components and Model Context Protocols (MCPs).
Develop a case study on cloud-native architecture
Create a project walkthrough highlighting a cross-team initiative you led. Focus on the architectural decisions made for high availability, security, and authentication, and explain how you would apply these principles to orchestrating LLM-based systems or agentic workflows.
Demonstrate proficiency in AI orchestration frameworks
Prepare to discuss your hands-on experience with LLM-based systems or orchestration frameworks. If you have built or integrated agents, be ready to explain the trade-offs in your design choices regarding latency, state management, and security.
Formulate strategic questions for the Perception team
Prepare thoughtful questions regarding the team's current challenges in agentic development, such as how they handle observability for multi-agent workflows or how they balance rapid innovation with the strict security requirements of Microsoft Security products.

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