Principal Customer Experience Engineering Manager

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. 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. 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.