AIOps Manager - Afternoon Shift

Company Overview
10Pearls is a global, purpose-driven  AI-Native digital engineering partner helping businesses re-imagine, ‎digitalize, and accelerate. As an end-to-end digital technology partner, 10Pearls helps businesses create future-proof, ‎transformative ‎digital products that leverage emerging technologies. ‎10Pearls' clients ‎include Global 2000 enterprises, high growth mid-size ‎businesses, and some of the most exciting ‎start-ups from industries like healthcare, fintech, ‎energy, education, ‎real estate, retail, and hi-tech. ‎Headquartered in the Washington DC metro area, 10Pearls has  product engineering and ‎software development centers in North America, Latin America, Europe, and South Asia. To learn more, visit  .  

Role
10Pearls is seeking an AIOps Manager to establish and lead the AIOps function in a greenfield environment. The role will be responsible for creating the operational framework for production AI systems, establishing observability and KPI standards, developing dashboards and alerting mechanisms, implementing AI FinOps controls, and defining operational processes for incidents, rollbacks, and model changes.
The ideal candidate will bring strong experience in AI operations, Azure monitoring, GenAI observability, AI FinOps, and model lifecycle management, along with the ability to establish operating models, engage business stakeholders, and provide executive-level reporting on AI usage, value, quality, and spend.

Responsibilities

• Establish the AIOps function and operating model from the ground up.
• Inventory and document production AI systems, models, services, and operational dependencies.
• Define and implement a GenAI observability framework covering latency, quality, hallucination, model drift, usage, and cost.
• Work with business and technical stakeholders to define KPIs, thresholds, SLAs, and alerting criteria.
• Design and implement Power BI dashboards providing visibility into AI system health, usage, value, quality, and spend.
• Leverage Azure Monitor, Application Insights, and Log Analytics/KQL for monitoring, troubleshooting, and operational insights.
• Establish AI FinOps practices, including token and spend attribution, budgeting, cost monitoring, optimization, and showback models.
• Utilize 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.
• Establish processes for Azure OpenAI model lifecycle management, including operational readiness and controlled model changes.
• Define and implement ITIL-aligned service management practices for AI workloads.
• Monitor AI usage, business value, operational performance, quality, and spend and provide regular reporting to leadership.
• Prepare monthly executive reports covering AI usage, value realization, operational performance, and spend.
• Partner with business owners to ensure KPIs and thresholds remain aligned with business objectives.
• Identify operational risks, cost optimization opportunities, and areas for improvement across the AI ecosystem.
• Establish strong documentation, knowledge transfer, and handover practices for AIOps processes, dashboards, and runbooks.
• Continuously improve the AIOps operating model as the organization's AI footprint evolves.

Requirements

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

Nice to Have

• FinOps Certified Practitioner certification.
• Experience implementing an AIOps operating model within a large enterprise.
• Experience with enterprise-scale GenAI platforms and production AI workloads.
• Experience developing AI budgeting, cost allocation, and showback frameworks.
• Experience with AI governance, model risk, and responsible AI operations.
• Experience working directly with CIO, CTO, CDO, or other senior technology/business leadership.
• Experience leading or mentoring technical teams.