AIOps Manager - Afternoon Shift

10Pearls, a global AI-native digital engineering partner, is seeking an experienced AIOps Manager to build and lead its AIOps function from the ground up. This role involves creating the operational framework for production AI systems, establishing observability standards, developing dashboards and alerting mechanisms, implementing AI FinOps controls, and defining processes for incident management, rollbacks, and model lifecycle changes. The ideal candidate will have strong expertise in AI operations, Azure monitoring, GenAI observability, AI FinOps, and model lifecycle management, with the ability to engage stakeholders and provide executive-level reporting on AI usage, value, quality, and spend.

Key Responsibilities

- Establish and develop the AIOps function and operating model in a greenfield environment.
- Inventory and document all production AI systems, models, services, and operational dependencies.
- Define and implement a GenAI observability framework covering latency, quality, hallucination, model drift, usage, and cost metrics.
- Collaborate with business and technical teams to define KPIs, thresholds, SLAs, and alerting criteria.
- Design and maintain Power BI dashboards to provide visibility into AI system health, usage, value, quality, and spend.
- Utilize Azure Monitor, Application Insights, and Log Analytics/KQL for monitoring, troubleshooting, and operational insights.
- Establish AI FinOps practices including token/spend attribution, budgeting, cost monitoring, optimization, and showback models.
- Leverage APIM AI Gateway and Azure AI Foundry capabilities to support AI usage and cost attribution.
- Develop and maintain incident management, rollback, and model-swap runbooks for production AI systems.
- Define processes for Azure OpenAI model lifecycle management, ensuring operational readiness and controlled model changes.
- Implement ITIL-aligned service management practices tailored for AI workloads.
- Monitor AI usage, business value, operational performance, quality, and spend, providing regular reports to leadership.
- Prepare monthly executive reports covering AI usage, value realization, operational performance, and spend.
- Partner with business owners to ensure KPIs and thresholds align with evolving business objectives.
- Identify operational risks, cost optimization opportunities, and continuous improvement areas across the AI ecosystem.
- Establish documentation, knowledge transfer, and handover practices for AIOps processes, dashboards, and runbooks.
- Continuously evolve the AIOps operating model as the organization’s AI footprint grows.

Required Qualifications

- 8–10+ years of experience in technology, cloud operations, AI/ML engineering, DevOps, MLOps, AIOps, or related fields.
- 3–5+ years of hands-on experience in AIOps, MLOps, AI platform operations, or production AI service management.
- Proven track record establishing or managing AI/ML operational frameworks in enterprise environments.
- Strong expertise in GenAI observability, including monitoring latency, quality, hallucination, drift, usage, and cost.
- Hands-on experience with Azure Monitor, Application Insights, and Log Analytics/KQL.
- Proficient in developing Power BI dashboards for operational and executive reporting.
- Experience implementing AI FinOps practices such as token consumption, spend attribution, budgeting, cost optimization, and showback models.
- Familiarity with Azure OpenAI and managing AI model lifecycles in production.
- Experience with APIM AI Gateway and/or Azure AI Foundry for AI workload management and cost attribution.
- Solid understanding of incident management, rollback procedures, model swaps, and operational runbooks.
- Knowledge of ITIL/service management practices applied to AI or technology operations.
- Strong skills in KPI definition, threshold management, alerting, and operational governance.
- Ability to translate technical data into clear executive-level insights and reporting.
- Excellent stakeholder management and communication skills across business, technical teams, and senior leadership.
- Demonstrated ability to work independently and establish processes in a greenfield setting.

Preferred Qualifications

- FinOps Certified Practitioner certification.
- Experience implementing AIOps operating models within large enterprises.
- Background working with enterprise-scale GenAI platforms and production AI workloads.
- Experience developing AI budgeting, cost allocation, and showback frameworks.
- Knowledge of AI governance, model risk management, and responsible AI operations.
- Experience collaborating directly with CIO, CTO, CDO, or other senior technology/business leaders.
- Proven leadership or mentoring experience with technical teams.

This role offers the opportunity to shape the future of AI operations within a dynamic, purpose-driven organization serving a diverse range of industries globally.