Principal Software Engineer

Microsoft•Redmond, WA

About The Position

Principal AI Engineer – AI Engineering Execution Excellence (Microsoft Security) Transform how AI engineers build, validate, and ship—using AI to increase throughput, quality, and customer impact. Microsoft Security (MSEC) Getting Customers Ready for AI (GR4AI) team is seeking a Principal AI Engineer to lead execution excellence for the AI engineering team. This role will translate the team’s vision and strategy into a disciplined operating system for building and shipping secure, enterprise-scale AI solutions with greater speed, quality, and predictability. The central mandate is to increase engineering throughput through an AI-native, NPF-transformative development model. You will redesign how engineers discover requirements, design systems, write and review code, create evaluations, investigate defects, document decisions, and operate services—embedding AI assistance and agents throughout the lifecycle rather than layering isolated tools onto existing practices. You will remain deeply hands-on while operating across organizational boundaries: establishing repeatable delivery mechanisms, removing systemic bottlenecks, creating reusable paved paths, and coaching engineers to work effectively with AI. In partnership with the team leader, who owns vision and strategy, you will turn priorities into executable plans and ensure the organization consistently converts ideas into trusted production outcomes.

Requirements

Bachelor's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.

Nice To Haves

Bachelor’s degree in Computer Science, Engineering, Data Science, a related technical field, or equivalent practical experience, plus substantial experience delivering complex production software and AI systems.
Demonstrated success improving execution across engineering teams through operating mechanisms, platform leverage, automation, and measurable constraint removal.
Deep hands-on expertise with modern AI engineering, including LLM application patterns, agent orchestration, retrieval, evaluation, data pipelines, and production operations.
Experience applying AI-assisted or agentic practices across the software-development lifecycle and establishing safeguards that make those practices dependable at scale.
Strong software-engineering skills in languages such as Python, C#, Java, or C++, with a record of shipping reliable cloud services and developer-facing platforms.
Proven ability to lead through influence, resolve cross-team execution tradeoffs, and communicate clearly with engineers, leaders, customers, and partners.
Experience embedding security, privacy, reliability, and responsible-AI principles into architecture, delivery, and engineering practices.
Experience architecting AI systems on Azure or another hyperscale cloud, including identity, data, compute, networking, observability, and deployment infrastructure.
Experience building internal developer platforms, coding agents, evaluation systems, or workflow automation that produced measurable improvements in engineering throughput.
Knowledge of flow and delivery measures such as cycle time, deployment frequency, work-in-progress, change-failure rate, recovery time, and developer experience.
Experience establishing evaluation programs for nondeterministic systems, including offline benchmarks, red teaming, online experimentation, and human-feedback mechanisms.
Track record of creating reusable platforms or standards that materially improved engineering velocity, product quality, or customer outcomes across an organization.

Responsibilities

Own Execution Excellence and Engineering Throughput: Translate the team leader’s vision and strategy into clear engineering priorities, executable plans, milestones, decision points, and accountable delivery rhythms.
Instrument the end-to-end engineering system to identify constraints in planning, design, implementation, review, evaluation, deployment, and operations; use evidence to improve flow rather than optimizing isolated activities.
Own measurable improvements in cycle time, deployment frequency, work-in-progress, quality, reliability, and engineer time spent on differentiated work, while avoiding output metrics that reward activity over customer value.
Transform Engineering with AI-Native Practices: Redesign the software-development lifecycle around AI-assisted and agentic workflows for discovery, design, coding, testing, evaluation, security review, documentation, incident response, and service operations.
Build reusable agents, context systems, evaluation harnesses, and paved paths that allow engineers to move from intent to validated production changes with less friction and stronger safeguards.
Establish standards for human oversight, provenance, secure tool use, data boundaries, review depth, and verification so increased velocity does not compromise trust, maintainability, or engineering judgment.
Lead Hands-On Delivery and Continuous Improvement: Work alongside engineers on the highest-leverage problems—prototype AI-native workflows, review critical designs and changes, diagnose systemic failures, and remove blockers that impede delivery.
Create reusable frameworks, templates, automation, and engineering standards that reduce cognitive load and enable teams to deliver faster without compromising reliability, security, or responsible AI.
Run disciplined learning loops through delivery reviews, retrospectives, experiments, and decision records; scale proven practices and retire processes or tools that do not improve outcomes.
Convert Priorities into Customer Outcomes: Partner with product, customer, and field teams to break strategic priorities into thin, testable increments that produce early evidence and shorten time to customer value.
Coordinate dependencies and resolve execution tradeoffs across Security, Azure, AI, Data, Research, and Customer Experience while keeping teams aligned to the established vision and strategy.
Connect delivery measures to customer adoption, task success, security posture, quality, reliability, time-to-value, and responsible-AI performance.
Own Production Trust and Responsible AI: Establish rigorous evaluation and release criteria for model quality, groundedness, safety, fairness, privacy, security, and abuse resistance.
Build telemetry and feedback loops that connect system behavior to customer outcomes, enabling rapid detection, learning, and continuous improvement.
Lead technical response to high-severity issues and ensure learnings become systemic improvements in architecture, testing, governance, and operations.