͏

This role bridges IT operations, data science, platform engineering, and business stakeholders to ensure successful delivery of Application management  solutions such as event correlation, anomaly detection, root-cause analysis, and automated remediation.

The role requires strong project management discipline, solid understanding of IT operations and monitoring landscapes, and the ability to manage cross‑functional technical teams delivering AIOps platforms and use cases.

Key Responsibilities

1. Program & Project Delivery

• Own end‑to‑end delivery of Application operations  projects, from initiation to stabilization and delivery .
• Define project scope, milestones, timelines, and success metrics aligned with business outcomes.
• Manage dependencies across infrastructure, applications, monitoring tools, and AIOps platforms.
• Ensure delivery within agreed cost, scope, schedule, and quality parameters.

2. Solution Governance

• Coordinate implementation of AIOps capabilities such as:
  • Event noise reduction and correlation
  • Anomaly detection and predictive alerts
  • Root cause analysis (RCA)
  • Automated remediation and runbook orchestration
• Work closely with platform teams using tools such as BMC Helix, Dynatrace, Splunk, or similar AIOps solutions (tool knowledge is conceptual, not hands-on).

3. Stakeholder & Customer Management

• Act as the primary point of contact for customers and senior stakeholders.
• Translate business and operational pain points into AIOps delivery roadmaps.
• Provide regular status updates, risk reports, and executive dashboards.
• Manage expectations and ensure alignment between delivery teams and customer objectives.

4. Cross‑Functional Team Leadership

• Lead and coordinate DevOps, SRE, monitoring, data engineering, and AIOps engineering teams.
• Enable collaboration between IT operations, AI/ML teams, and service management teams.
• Drive Agile or hybrid delivery models (Scrum, SAFe, Kanban, or Waterfall as applicable).

5. Risk, Issue & Change Management

• Identify delivery risks related to data quality, integrations, model accuracy, and operational readiness.
• Proactively manage issues impacting AIOps adoption and value realization.
• Govern change requests and ensure controlled rollout into production environments.

6. Service Transition & Value Realization

• Ensure smooth transition of AIOps solutions into BAU operations.
• Track benefits such as MTTR reduction, alert noise reduction, operational cost savings, and SLA improvements.
• Support continuous improvement and roadmap evolution post‑implementation.