Senior Customer Experience Program Manager

Microsoft•Redmond, WA

4d•$119,800 - $261,000

About The Position

The Azure Engineering Operations (EngOps) team is seeking a Senior Customer Engineering Program Manager – AI Operations to define and operationalize the next generation of AI-powered support systems for Azure Engineering Direct (AED). This role involves combining program leadership, data-driven decision making, and applied AI strategy to build intelligent support experiences that proactively resolve issues, empower engineers, and transform customer outcomes. The goal is to transition AED from reactive support to predictive, AI-driven operations by scaling agentic AI systems that autonomously diagnose, recommend, and resolve issues, thereby driving improvements in case volume reduction, MTTR, and customer satisfaction.

Requirements

Bachelor's Degree in Computer Science, Engineering, Data Science, Math, Business, or related field AND 4+ years experience in engineering, product/technical program management, data analysis, or product development OR equivalent experience.
Ability to meet Microsoft, customer and/or government security screening requirements.
Must pass the Microsoft Cloud Background Check upon hire/transfer and every two years thereafter.

Nice To Haves

Master's Degree in Computer Science, Engineering, Data Science, Math, Business, or related field AND 6+ years experience in engineering, product/technical program management, data analysis, or product development OR Bachelor's Degree in Computer Science, Engineering, Data Science, Math, Business, or related field AND 8+ years experience in engineering, product/technical program management, data analysis, or product development OR equivalent experience.
6+ years of experience in program management, product management, engineering, or technical operations.
Hands-on experience with AI/ML systems, LLMs, or AI-powered products (e.g., copilots, automation tools, RAG systems).
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.
Strong understanding of cloud platforms (preferably Azure) and support operations.
Experience using data and metrics to drive decisions and measure impact.
Experience in support engineering, SRE, or ITSM environments.
Knowledge of AI evaluation frameworks, telemetry, and experimentation practices.
Experience working with global, distributed engineering and customer-facing teams.
Strong executive communication and storytelling skills.

Responsibilities

Define and execute the AI Operations strategy for AED, aligning to business goals such as case deflection, resolution time reduction, and support scalability.
Translate customer pain points and operational gaps into AI-powered solutions including agent workflows, copilots, and automation systems.
Partner with engineering teams to bring agentic AI services (LLMs, RAG, multi-agent orchestration) from concept to production.
Drive adoption of AI agents and copilots across ACE workflows (triage, diagnostics, mitigation, knowledge retrieval).
Define requirements for multi-agent systems, tool integrations, and workflow orchestration to enable autonomous issue resolution.
Ensure solutions are grounded in real-time Azure signals, telemetry, and knowledge systems.
Own the end-to-end lifecycle of AI-powered operational capabilities: design, experimentation, deployment, monitoring, and continuous improvement.
Establish evaluation frameworks (success metrics, golden datasets, feedback loops) to measure AI effectiveness, quality, and safety.
Embed Responsible AI practices ensuring systems are safe, compliant, and aligned to Microsoft standards.
Evolve traditional delivery excellence frameworks into AI-augmented operational models.
Standardize and scale AI-enabled support processes, tooling, and playbooks across AED.
Continuously improve operational efficiency through automation, intelligence, and insights.
Define and track AI Ops KPIs such as Case deflection and avoidance, Mean time to resolution (MTTR) and Customer experience metrics.
Partner with data and engineering teams to build observability systems (metrics, logging, tracing) to monitor AI and operational performance.
Lead cross-org initiatives spanning engineering, product, support, and field teams.
Act as the bridge between AI engineering teams and customer engineering delivery.
Drive alignment on roadmaps, priorities, and success criteria across stakeholders.
Engage directly with AED customers and ACEs to identify unmet needs and high-friction experiences.
Incubate and scale AI-first capabilities that reduce toil and improve customer self-service.
Champion a culture of experimentation and rapid iteration.