Principal Architect

Microsoft•Redmond, WA

About The Position

Microsoft Security is developing Perception, an advanced Agentic AI platform intended to help security and IT teams operate at the speed and scale of AI while following Microsoft’s responsible AI principles. The Principal Architect will define and drive the platform’s architectural vision and long-term technical strategy, with particular emphasis on secure, reliable, and scalable operation across multiple tenants and diverse enterprise environments. The role provides architectural leadership across generative AI, Retrieval-Augmented Generation (RAG), memory, reasoning, agentic and multi-agent systems, identity and authorization, AI safety, distributed cloud systems, and platform infrastructure. The architect will establish reference architectures, principles, system boundaries, integration patterns, and reusable abstractions that accommodate varying tenant configurations, infrastructure, security boundaries, compliance constraints, and deployment models. The role also guides existing production architectures toward consistent, modular, configurable, and reusable platform capabilities. Working with engineering and product leaders, architects, security, privacy and compliance teams, and partner organizations, the Principal Architect will translate customer, product, security, and platform requirements into scalable architectures and long-term technology investments. The role serves as a technical authority for critical decisions and tradeoffs, drives architectural convergence across teams, and mentors senior engineers and architects. This is a Software Engineering IC5 position.

Requirements

Bachelor’s degree in Computer Science, Engineering, or a related technical field and 6+ years of technical engineering experience designing and building complex software systems, distributed systems, or cloud platforms, or equivalent experience.
Ability to meet Microsoft, customer, and/or government security screening requirements, including passing the Microsoft Cloud Background Check upon hire or transfer and every two years thereafter.
Significant experience architecting large-scale distributed systems, multi-tenant services, enterprise platforms, cloud infrastructure, or production AI-powered systems.
Experience establishing reference architectures, architectural principles, platform standards, system boundaries, and reusable patterns across multiple engineering teams or organizations.
Experience designing strong tenant isolation across identity, authorization, data, compute, configuration, networking, and operational boundaries.
Experience defining architecture for generative AI systems, including model orchestration, RAG, semantic retrieval, memory, context management, reasoning, tool use, evaluation, grounding, safety, and production operations.
Experience architecting agentic or multi-agent systems, including planning, tool invocation, task orchestration, memory, state management, delegation, agent communications, policy enforcement, and bounded autonomous execution.
Deep understanding of identity, authentication, authorization, RBAC, least privilege, delegated authorization, workload, service and agent identities, and multi-tenant security boundaries.
Experience defining secure architectural patterns for integrating AI systems with enterprise data, services, APIs, plugins, tools, and distributed execution environments.
Experience designing RAG architectures, including ingestion, indexing, embeddings, retrieval, re-ranking, provenance, grounding, authorization-aware retrieval, and evaluation.
Experience with modern cloud architecture, including Kubernetes, containerized services, compute, storage, networking, service-to-service communication, configuration management, Infrastructure as Code, automated deployment, and CI/CD.
Experience architecting highly available and resilient services, including handling dependency failure, capacity constraints, throttling, graceful degradation, fault isolation, recovery, and disaster scenarios.
Experience defining observability, diagnostics, health monitoring, deployment validation, and operational architecture for large-scale distributed systems.
Experience identifying architectural coupling and evolving production systems toward modular, reusable, configurable, extensible, and scalable platform architectures.
Experience evaluating tradeoffs involving security, reliability, scalability, performance, cost, operational complexity, developer productivity, and time-to-market.
Demonstrated ability to influence technical strategy and align engineering organizations around shared architectural direction without direct organizational authority.
Strong technical communication skills, including explaining complex architectures and tradeoffs to engineering teams, architects, product leaders, security experts, and senior leadership.
Demonstrated ability to mentor senior technical leaders and raise architectural and engineering capabilities across an organization.

Nice To Haves

Master’s degree in Computer Science, Engineering, or a related technical field and 8+ years of relevant technical engineering experience, or a bachelor’s degree in one of these fields and 12+ years of relevant technical engineering experience, or equivalent experience.

Responsibilities

Define the Perception Platform’s architectural vision and long-term technical strategy, including principles and reference architectures for production-grade AI capabilities.
Define scalable multi-tenant architectures and patterns for tenant isolation, identity boundaries, authorization, configuration, data protection, deployment, sovereignty, compliance, and operations.
Establish system boundaries and reusable abstractions across compute, storage, networking, identity, configuration, deployment, observability, data, AI execution, and service integration.
Drive architectural convergence across engineering teams by identifying divergent implementations and establishing common patterns, interfaces, contracts, and platform capabilities.
Define automation-first architecture for tenant provisioning, configuration, deployment, validation, upgrades, migrations, lifecycle management, and operational readiness.
Design for variation in tenant configuration, service availability, infrastructure topology, networking, identity, security, data residency, sovereignty, and compliance without spreading environment-specific assumptions through application architecture.
Establish architecture for scalable RAG systems, including ingestion, indexing, embeddings, retrieval, context construction, grounding, provenance, re-ranking, evaluation, data governance, and tenant and authorization boundaries.
Define architecture and principles for agentic and multi-agent systems, including planning, reasoning, tool use, task decomposition, delegation, collaboration, state and memory, human-in-the-loop patterns, failure recovery, bounded autonomy, and communication.
Define identity, authentication, and authorization architecture for AI and agentic systems, including user, workload, service, and agent identities; RBAC; delegated authorization; least privilege; scoped permissions; and secure propagation of authorization context.
Establish guardrails that preserve tenant isolation and authorization across retrieval, reasoning, planning, memory, and execution, and validate agent plans and tool interactions against security, authorization, safety, privacy, and compliance policies.
Define AI safety, security, and trust-boundary architecture, including defense in depth for prompt injection, unsafe tool invocation, cross-tenant exposure, untrusted content, provenance, policy enforcement, and interactions among models, agents, tools, services, and enterprise data.
Establish architecture for AI memory and state management, covering isolation, authorization-aware retrieval, lifecycle, retention, deletion, provenance, privacy, compliance, and safe reuse of context.
Define architectural expectations for automated validation, testing, and conformance, including functional correctness, tenant isolation, authorization, security, reliability, deployment readiness, and compatibility across environments.
Establish observability, monitoring, diagnostics, and operational architecture to understand platform health, identify systemic failures, distinguish platform-wide from tenant-specific issues, and troubleshoot distributed services and AI workflows.
Define strategies for resilience, scale, capacity, dependency management, graceful degradation, disaster recovery, and failure isolation across globally distributed AI and cloud services.
Identify systemic architectural dependencies and guide production systems toward modular, configurable, reusable, secure, and scalable platform architectures, balancing long-term direction with practical migration strategies.
Lead reviews of critical platform investments and advise on build-versus-reuse decisions, service boundaries, data and API contracts, technology selection, cross-service dependencies, and architectural tradeoffs.
Partner with engineering and product leadership to translate customer and business requirements into architectural roadmaps and strategic technology investments.
Influence architecture across Microsoft Security and related platform organizations, aligning direction and establishing reusable patterns while preventing unnecessary duplication and fragmentation.
Mentor and develop senior engineers and architects, raise organizational architectural capability, and promote rigorous design, technical depth, engineering excellence, and long-term systems thinking.