Senior Customer Experience Program Manager

Microsoft•Redmond, WA

1d•$119,800 - $234,700

About The Position

The Azure Engineering Operations (EngOps) team works to create scalable, frictionless support experiences for Microsoft Cloud customers. Within EngOps, the ACES organization is advancing AI-first support delivery through agentic AI, LLM-powered systems, and real-time operational intelligence. The Senior Customer Engineering Program Manager – AI Operations will define and operationalize AI-powered support systems for Azure Engineering Direct (AED), moving support from reactive to predictive operations. The role focuses on scaling systems that can diagnose, recommend, and resolve issues, with the goal of reducing case volume and resolution times, improving customer satisfaction, and establishing AI Ops as a core part of support delivery.

Requirements

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

Nice To Haves

Master's degree in Computer Science, Engineering, Data Science, Math, Business, or a related field and 6+ years of experience in engineering, product or technical program management, data analysis, or product development; or a bachelor's degree in a related field and 8+ years of such experience; 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 such as copilots, automation tools, or RAG systems.
Familiarity with agentic AI concepts, including 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, aligned with goals such as case deflection, faster resolution, 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 take agentic AI services—including LLMs, RAG, and multi-agent orchestration—from concept to production.
Drive adoption of AI agents and copilots across ACE workflows such as triage, diagnostics, mitigation, and knowledge retrieval.
Define requirements for multi-agent systems, tool integrations, and workflow orchestration for autonomous issue resolution.
Ensure solutions use real-time Azure signals, telemetry, and knowledge systems.
Own the lifecycle of AI-powered operational capabilities, from design and experimentation through deployment, monitoring, and continuous improvement.
Establish evaluation frameworks, including success metrics, golden datasets, and feedback loops, to measure AI effectiveness, quality, and safety.
Embed Responsible AI practices to ensure systems are safe, compliant, and aligned with Microsoft standards.
Evolve delivery excellence frameworks into AI-augmented operational models.
Standardize and scale AI-enabled support processes, tooling, and playbooks across AED.
Improve operational efficiency through automation, intelligence, and insights.
Define and track AI Ops KPIs, including case deflection and avoidance, mean time to resolution, and customer experience metrics.
Partner with data and engineering teams to build observability systems using metrics, logging, and tracing to monitor AI and operational performance.
Lead cross-organizational initiatives across engineering, product, support, and field teams.
Bridge AI engineering teams and customer engineering delivery, aligning stakeholders on roadmaps, priorities, and success criteria.
Engage 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 experimentation and rapid iteration.