Principal Customer Experience Engineering Manager

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. 

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. 

Required qualifications