Assistant Manager - Cloud - Product Development

Core Responsibilities:

Program Ownership & Delivery

• Own end-to-end execution of complex, cross-portfolio programs across multiple product lines (IaaS, PaaS & AI)
• Define and maintain program charters, milestones, critical paths, and success metrics.
• Maintain program dashboards, risk registers, and status reports.
• Drive quarterly and monthly execution cadence: operating reviews, weekly tracking, and escalation management.
• Proactively identify, manage & mitigate delivery risks, CFT (cross functional teams) dependencies, trade-offs and bottlenecks, and drive them to closure.

Operational Excellence & Scale

• Establish program-level KPIs: delivery predictability, launch quality, adoption readiness, and post-launch stability.
• Drive incident learnings and systemic fixes back into platform roadmaps.
• Continuously improve execution mechanisms, tooling, and ways of working across the cloud organization.
• Support leadership with clear, written execution narratives and dashboards.

Cross-Functional Coordination

• Act as the single-threaded owner across Product Management, Engineering, Service delivery and Operations.
• Drive alignment between long-term roadmap priorities and short-term execution realities.
• Track cross-team dependencies and ensure hand-offs do not become execution gaps.
• Ensure clear communication to senior leadership through written narratives, dashboards, and executive updates.

Basic Qualifications

• Strong understanding of distributed systems, cloud infrastructure, and platform services.
• Proven experience managing large, multi-team programs in technology organizations with high operational and business impact.
• Excellent written and verbal communication skills ability to influence without authority.
• Experience working with Agile, Scrum, or hybrid execution models at scale.
• Strong planning, organization, and stakeholder management skills.
• Ability to operate effectively in high ambiguity, fast-moving environments.

Preferred Qualifications

• Hands-on experience with cloud platforms (compute, storage, networking, Kubernetes, databases).
• Exposure to AI/ML platforms, data infrastructure, or GPU-based workloads.
• Prior experience working in a hyperscaler or large-scale SaaS/cloud provider.