Job Description

Lead high-severity Azure incidents with strong command presence and clear decision-making under pressure. Drive the end-to-end incident lifecycle, including detection, triage, mitigation, communication, and post-incident learning. Partner across Azure product groups, EngOPS, and field teams to accelerate diagnosis, reduce time-to-mitigation, and drive sustainable fixes. Represent the voice of the customer by surfacing systemic issues, platform gaps, and reliability risks to engineering teams. Drive operational maturity through repeatable processes, strong governance, high-quality execution, and measurable reliability metrics. Identify live-site patterns and hotspots across services and lead cross-team action plans to address them. Convert customer and incident pain points into automation, AI-assisted workflows, and process improvements. Lead or co-own pilots, proofs-of-concept, and tech accelerators that enhance incident response velocity and quality. Contribute to internal playbooks, frameworks, and tooling that leverage AI/ML for improved live-site management. Hands-on experience building or contributing to AI/ML solutions is required; Python experience is a plus. 10+ years of experience in incident management, service engineering, program management, or related technical roles. Strong track record commanding high-pressures, complex, cross-team incidents across cloud or large-scale distributed systems. 5+ years of hands-on experience working with cloud technologies (Azure preferred). Strong understanding of Azure architecture, core services, and internal operational workflows. Exceptional communication skills, with the ability to simplify complex technical issues for senior executives and customers. Experience collaborating in matrixed engineering environments with diverse stakeholders (PG, EngOPS, Field, GPMs, PMs, SREs). Strong analytical skills; ability to drive insight from data and influence direction through evidence. Proven experience driving pilots, building prototypes, or contributing to innovation in live-site or automation scenarios. Demonstrated experience in AI/ML-based solutions-automation, anomaly detection, NLP, or reliability tooling. Exposure to Power BI, Kusto (KQL), or other analytical tooling. Relevant technical degree (CS, Engineering, IT) or equivalent experience. Experience with scripting or automation using Python is preferred.

Skills Required