Principal AI Architect - M365 IC3 Team (Intelligent Conversation and Communications Cloud)

Microsoft•Redmond, WA

5h•$142,800 - $304,200

About The Position

We are looking for a Principal AI Architect to help define, build, and scale the evaluation systems that shape the future of AI products. This role sits at the intersection of engineering, applied science, product architecture, and AI evaluation. The ideal candidate has deep technical judgment, strong systems thinking, hands-on experience evaluating LLMs, and the ability to translate emerging AI capabilities into reliable product experiences. This role is especially important for agentic AI systems, where product behavior is often nondeterministic, context-dependent, and difficult to evaluate with traditional testing alone. The person in this role will help teams build a deep understanding of how agents behave, where they succeed, where they fail, which gaps matter most, and where those gaps should be addressed: in prompts, tools, orchestration, retrieval, ranking, product UX, safety systems, or core code. The Principal AI Architect will make AI product quality measurable, actionable, and deeply integrated into how teams build and ship. They will help teams evaluate product direction before code is complete, validate quality before launch, and continuously measure performance after release. They will give teams confidence in how agentic systems behave, where nondeterminism creates risk, which gaps matter, and where to address them. Their work will ensure that evaluation is not an afterthought, but a core part of the product lifecycle, engineering system, and release decision process. Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Requirements

Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research) OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.
Demonstrated experience evaluating LLMs, including designing eval datasets, defining quality metrics, analyzing model behavior, identifying failure modes, and using results to guide product or system improvements.
Understanding of modern AI systems, including LLMs, agentic systems, RAG, ML pipelines, evaluation methodology, experimentation, and product telemetry.
Ability to architect complex systems where multiple components, services, models, data flows, tools, retrieval systems, and product surfaces interact.
Experience evaluating or building nondeterministic AI systems where quality must be understood statistically, behaviorally, and through product impact.
Experience bringing ML, DS, LLM, or applied science concepts into production systems and product codebases.
Ability to integrate evaluation into engineering systems such as CI/CD, build pipelines, release gates, dashboards, and monitoring workflows.
Understanding of search, retrieval, grounding, relevance, ranking, and enterprise RAG concepts.
Ability to define technical strategy, product-quality metrics, milestones, and execution plans across teams.
Coding and technical design skills, with the ability to work directly in product codebases when needed.
Effective communication skills with the ability to influence engineers, scientists, product managers, and executives.
Track record of leading ambiguous, cross-functional technical initiatives from concept through delivery.

Nice To Haves

Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 9+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.
5+ years experience creating publications (e.g., patents, libraries, peer-reviewed academic papers).
2+ years experience presenting at conferences or other events in the outside research/industry community as an invited speaker.
5+ years experience conducting research as part of a research program (in academic or industry settings).
3+ years experience developing and deploying live production systems, as part of a product team.
3+ years experience developing and deploying products or systems at multiple points in the product cycle from ideation to shipping.
Experience evaluating LLM-powered products, agents, enterprise search, recommendation systems, or generative AI applications.
Experience with offline evals, online experimentation, human evaluation, red teaming, synthetic data, model monitoring, RAG evaluation, and agent behavior analysis.
Experience with evaluation dashboards, scorecards, quality reporting, product-health monitoring, or data visualization systems.
Familiarity with responsible AI, safety, reliability, privacy, security, permissions, compliance, and enterprise-readiness considerations for AI systems.
Experience building evaluation platforms, experimentation systems, model observability, agent evaluation infrastructure, or product-quality infrastructure.
Experience operating at principal, architect, or senior technical leadership level.

Responsibilities

Define the technical vision and architecture for AI, LLM, RAG, and agent evaluation systems across the product lifecycle.
Design evaluation frameworks that assess product quality before implementation is complete, during development, at launch, and post-ship.
Build an understanding of how nondeterministic agentic systems behave across tasks, users, contexts, tools, and product surfaces.
Identify behavioral gaps, failure modes, model limitations, retrieval issues, orchestration defects, and product-quality risks before they become customer-impacting issues.
Determine where issues should be addressed across the system: model behavior, prompts, tool use, search and retrieval, ranking, grounding, orchestration, UX, policy, telemetry, or product code.
Partner with engineering, applied science, and data science teams to bring ML, DS, LLM, RAG, and agent evaluation methods directly into product codebases and development workflows.
Integrate evals into build pipelines, release gates, experimentation systems, and engineering workflows so evaluation becomes a standard part of how products are built and shipped.
Make evaluation results easy to access, interpret, and act on through dashboards, scorecards, quality reports, and product-health views.
Build systems that connect product telemetry, offline evaluation, human judgment, automated evals, experimentation, RAG quality, agent behavior, and customer-quality signals.
Understand and evaluate enterprise search, RAG, grounding, indexing, ranking, permissions, freshness, and relevance systems for products such as Copilot.
Translate ambiguous product goals into measurable evaluation strategies, success criteria, timelines, and technical plans.
Drive architecture decisions across components, services, data pipelines, model interfaces, search systems, retrieval layers, evaluation harnesses, dashboards, and reporting systems.
Work with product leaders to prioritize evaluation investments and align them with product milestones and release decisions.
Mentor senior engineers and applied scientists on building reliable, scalable, and reusable evaluation infrastructure.
Stay current with LLM evaluation methods, agentic systems, RAG evaluation, benchmark design, prompt/model behavior, experimentation, and responsible AI practices.
Help evaluate products before the code is fully ready, before launch, and after they ship.
Work across product, engineering, applied science, and data science teams to bring rigorous LLM, RAG, agent, and AI evaluation practices into the product lifecycle.
Help IC3 and partner teams understand whether AI systems are working as intended, where they fail, how they improve, and what it takes to ship them responsibly at scale.