Principal Software Engineer

Microsoft•Redmond, WA

18h

About The Position

The Trusted Servicing Experiences / Supportability & Customer-Centric Engagement and Engineering Direct Team is seeking a Principal Software Engineer to own architecture for secure, enterprise-scale platforms spanning supportability, customer-centric engineering, escalation intelligence, and operational analytics. The role sets technical direction across software, data, and agentic AI systems, including AI harnesses and sandbox environments, distributed backend services, and reusable AI capabilities. It operates with broad autonomy, aligning multiple teams and establishing foundations for security, privacy, scale, and enterprise adoption. The engineer partners with customers and internal stakeholders to deliver trusted solutions in support of Microsoft’s mission. The role emphasizes responsible innovation, shared progress, and a growth mindset.

Requirements

Bachelor’s degree in Computer Science or a related technical field and 6+ years of technical engineering experience coding in languages such as C, C++, C#, Java, JavaScript, or Python, 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.

Nice To Haves

10+ years of technical engineering experience, including 5+ years owning architecture for complex, distributed enterprise systems and data platforms.
Experience architecting and delivering large, business-critical initiatives from design through production operations.
Hands-on backend engineering expertise, including coding, testing, debugging, reliability, performance, and maintainability.
Experience building enterprise AI platforms, including AI harnesses, sandbox environments, and reusable capabilities using frameworks such as AutoGen, Semantic Kernel, or LangGraph.
Experience developing production-grade agentic AI systems that reason over data, operate across distributed systems, and generate automated insights.
Proven track record leading end-to-end architecture for at least two large, business-critical initiatives spanning design, build, deployment, and operations.
Deep hands-on expertise in software engineering fundamentals, including coding, design, testing, debugging, reliability, and maintainability.
Deep data engineering experience, including Python/PySpark, ETL/ELT pipelines, data modeling, quality, governance, lineage, and large-scale analytics platforms.
Expertise in security, privacy, compliance, least-privilege controls, auditability, and secure deployment within enterprise environments.
Demonstrated ability to align teams, influence technical roadmaps, establish reusable architecture standards, and mentor senior engineers.

Responsibilities

Lead adoption of AI-native engineering practices in the software development lifecycle, including Responsible AI controls for AI-generated design and code artifacts and workflow automation.
Influence strategic prioritization with data-driven recommendations, KPI frameworks, and experimentation-informed decisions.
Define and own end-to-end architecture for software and data platforms supporting engineering health, supportability, customer escalation, and operational intelligence.
Lead architecture and design reviews for complex distributed services and data systems, making tradeoff decisions across scalability, resiliency, performance, security, privacy, and cost.
Establish architecture patterns, standards, and guardrails for code quality, testing strategy, observability, deployment safety, and dependency management across teams.
Architect and govern data foundations, including data models, schemas, lineage, contracts, semantic layers, quality controls, and governance mechanisms.
Drive telemetry-first engineering through instrumentation, monitoring, anomaly detection, root-cause workflows, and incident-prevention practices.
Lead live-site reliability strategy, including DRI readiness, service health indicators, failure-mode analysis, postmortems, and continuous reliability improvements.
Partner with product management, engineering, support/escalation, and leadership stakeholders to translate ambiguous business needs into architecture roadmaps and milestones.
Lead cross-team dependency planning and execution, negotiate ownership boundaries, and align partner teams to shared delivery outcomes.
Embed secure-by-design principles, least-privilege controls, data-handling controls, and regulatory requirements into architecture and operations to ensure compliance and auditability.
Mentor senior engineers and technical leads, raising architectural quality and decision velocity through clear technical direction and reusable patterns.