Job description

Overview The ACES CØDE organization is transforming Azure support from reactive case handling to AI-first, autonomous demand resolution. CØDE exists to engineer operational demand out of the system by converting customer signals, unresolved cases, autonomous-resolution failures, and support-delivery friction into durable improvements in product instructions, diagnostics, automation, AI workflows, and service-area readiness. We are seeking a Principal Customer Experience Engineering Manager, AI Ops US to lead a US-based team of Customer Engineers responsible for driving the day-to-day hill climb of AI-first support delivery for Broad Commercial customers. This leader will manage US-based FTEs across Redmond and remote locations, ensuring service-area leads are accountable for measurable improvements in autonomous resolution, engineer efficiency, community-led resolution, customer satisfaction, and operational quality. This role is a critical leadership position in the Broad Commercial AI Ops model. While delivery partners execute frontline support delivery, this team will own the engineering-led improvement loop: analyzing cases that fall out of FACS into human-in-the-loop handling, understanding why autonomous resolution did not succeed, identifying product-instruction and diagnostic gaps, and translating customer and engineer signals into improvements that make FACS and agentic workflows better every day. The team priorities include improving FACS case closure to 35%, scaling community contributions to 40%, improving engineering efficiency by 5X to 10X, and reducing volume to 73.3K. Responsibilities Key Responsibilities • Lead and develop the AI Ops US team. Manage, coach, and grow a high-performing team of US-based Customer Engineers who serve as service-area leads across Azure technical domains. Create clarity of ownership, measurable priorities, and a strong culture of accountability, learning, and AI-first engineering execution. • Drive autonomous-resolution hill climb. Ensure the team systematically reviews cases that were not autonomously resolved by FACS, identifies why each turn or workflow failed, and converts those learnings into improvements in product instructions, diagnostics, workflow design, knowledge quality, routing, and model effectiveness. The CØDE operating model explicitly emphasizes learning from failed autonomous resolution, human takeover, and customer signals to close gaps and hill climb. • Improve FACS outcomes for Broad Commercial. Partner with AI Ops PM team to improve FACS adoption, governance, operational quality, and case-closure performance toward the 35% autonomous-resolution goal. • Increase engineer efficiency through AI and automation. Partner with AI Ops PM team and delivery stakeholders to identify opportunities for automation, agentic AI infusion, workflow simplification, reporting improvements, and process optimization that improve engineering throughput and reduce toil across service areas and delivery partners. • Scale Community Champion success by service area. Ensure each service-area lead contributes to the Community Champions motion by engaging experts, improving community-led resolution quality, and helping scale community contribution toward the 40% target with strong 90%+ CSAT and verified-answer outcomes. • Partner deeply across the India and US AI Ops model. Work closely with India AI Ops leader, and with US and India service-area leads to create one operating rhythm, shared standards, consistent scorecards, and clear handoffs between service-area engineering improvement and delivery execution. • Own service-area operational health. Drive disciplined review of CSAT, IRT, escalations, throughput, CPT/DTC, FACS outcomes, and volume-reduction drivers for US-owned service areas. Service-area accountability for CSAT above 92%, IRT below 4 hours, zero case escalations to CSS, 10x throughput improvement, and KPI reviews. • Convert customer and delivery signals into engineering action. Ensure unresolved cases, DSAT patterns, low-CSAT FACS closures, escalation trends, and delivery-partner feedback are translated into prioritized engineering asks, product-group feedback, content improvements, automation opportunities, and measurable closed-loop outcomes. The Broad Commercial and Sovereign demand engineering program is explicitly accountable for converting unresolved cases, customer feedback, and DSAT into engineering insights that improve next-generation autonomous resolution capabilities. • Drive operating rhythm and executive-ready accountability. Establish clear weekly and monthly review mechanisms for progress against autonomous resolution, volume reduction, community contribution, engineer efficiency, service-area health, and stakeholder commitments. The CØDE model emphasizes execution rigor through weekly ops reviews, bi-weekly delivery reviews, SteerCo governance, and monthly executive reviews. Success Measures Success in this role will be measured by the leader’s ability to build a high-performing US AI Ops team that improves autonomous resolution, reduces support demand, increases engineer throughput, improves customer experience, and strengthens the closed-loop learning system between support delivery, FACS behavior, service-area insights, and engineering action. The relevant CØDE and PCS measures include support volume reduction, 200K+ autonomous resolutions in FY27, 40% toil reduction, 35% autonomous resolution rate, 73.3K PCS volume target, 5X to 10X engineer efficiency, and 40% community contribution. Qualifications Required qualifications • Bachelor's Degree in Engineering, Computer Science, Information Technology (IT), Data Analytics/Science, Artificial Intelligence (AI), or related field AND 6+ years experience in technology industry, cloud, technical support, and/or customer experience engineering • OR equivalent experience. • 1+ year(s) of customer facing experience. Other Requirements • Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings: • Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud Background Check upon hire/transfer and every two years thereafter. Preferred qualifications • Master's Degree in Engineering, or related field AND 8+ years experience in technology industry, cloud, technical support, and/or customer experience engineering OR Bachelor's Degree in Engineering, Computer Science, Information Technology (IT), Data Analytics/Science, Artificial Intelligence (AI), or related field AND 12+ years experience in technology industry, cloud, technical support, and/or customer experience engineering OR equivalent experience. • 3+ years of customer facing experience. • 5+ years people management experience. • 6+ years of experience in program management, product management, engineering, or technical operations • Experience with AI/ML systems, LLMs, or AI-powered products (e.g., copilots, automation tools, RAG systems) • Experience improving autonomous-resolution, self-help, diagnostics, or AI-assisted support workflows. • Knowledge of AI evaluation, feedback loops, telemetry, experimentation, and Responsible AI practices. • Experience leading AI-driven transformation programs that combine people leadership, operational governance, engineering improvement, and AI adoption. • Demonstrated ability to use data, telemetry, and customer signals to identify patterns, prioritize improvements, and measure business impact. • Familiarity with agentic AI concepts such as multi-agent orchestration, prompt design, and AI workflows • Experience driving end-to-end delivery of complex, cross-functional initiatives • Understanding of cloud platforms (preferably Azure), support operations, customer escalation patterns, and case quality drivers • Experience using data and metrics to drive decisions and measure impact • Experience working with global, distributed engineering and customer-facing teams • Effective Executive communication skills with the ability to translate operational detail into clear business outcomes, risks, and decisions. • Experience managing technical teams and developing engineering talent across distributed locations. • Cross-functional leadership skills, including partnership with PM, engineering, product groups, delivery partners, and support organizations. • Demonstrated success driving organizational change and transformation programs. Customer Experience Engineering M5 - The typical base pay range for this role across the U.S. is USD $142,800 - $274,800 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $188,000 - $304,200 per year. Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: careers.microsoft.com This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled. Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.

Requirements

Must-haves

Improve FACS outcomes for Broad Commercial. Partner with AI Ops PM team to improve FACS adoption, governance, operational quality, and case-closure performance toward the 35% autonomous-resolution goal.
Success Measures
1+ year(s) of customer facing experience.
Other Requirements
3+ years of customer facing experience.
5+ years people management experience.
6+ years of experience in program management, product management, engineering, or technical operations
This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.

Nice-to-haves

Experience with AI/ML systems, LLMs, or AI-powered products (e.g., copilots, automation tools, RAG systems)
Experience improving autonomous-resolution, self-help, diagnostics, or AI-assisted support workflows.
Knowledge of AI evaluation, feedback loops, telemetry, experimentation, and Responsible AI practices.
Experience leading AI-driven transformation programs that combine people leadership, operational governance, engineering improvement, and AI adoption.
Demonstrated ability to use data, telemetry, and customer signals to identify patterns, prioritize improvements, and measure business impact.
Familiarity with agentic AI concepts such as multi-agent orchestration, prompt design, and AI workflows
Experience driving end-to-end delivery of complex, cross-functional initiatives
Understanding of cloud platforms (preferably Azure), support operations, customer escalation patterns, and case quality drivers
Experience using data and metrics to drive decisions and measure impact
Experience working with global, distributed engineering and customer-facing teams
Effective Executive communication skills with the ability to translate operational detail into clear business outcomes, risks, and decisions.
Experience managing technical teams and developing engineering talent across distributed locations.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

azureaiml

Platform: eightfold
Effort: medium