Manager - Service Delivery & Management
About The Company
Tata Communications Redefines Connectivity with Innovation and IntelligenceDriving the next level of intelligence powered by Cloud, Mobility, Internet of Things, Collaboration, Security, Media services and Network services, we at Tata Communications are envisaging a New World of Communications
Broad Outline Of The Role
Responsible for managing the end-to-end delivery of customer projects and platform implementations in Linux Infrastructure, Kubernetes (K8s), AI/ML Platforms, and GPU-as-a-Service (GPUaaS) environments. This includes project planning, technical coordination, stakeholder management, deployment oversight, risk management, and service transition activities. The role requires collaboration across Engineering, Product, Operations, and Customer teams to ensure successful delivery of AI and cloud-native solutions within agreed timelines, quality standards, and budget constraints. This is a techno-functional role with direct responsibility for project execution, customer satisfaction, platform stability, and business outcome realization.
Minimum Qualifications & Experience
Bachelor's Degree in Engineering, Computer Science, Information Technology, or equivalent qualification. 6 to 11 years of experience in Linux Administration, Cloud Infrastructure, Kubernetes, AI Infrastructure, Project Delivery, or related domains. Experience in customer-facing project implementation and cloud-native environments. Exposure to GPU technologies, AI/ML workloads, or High-Performance Computing (HPC) environments is preferred.
Other Knowledge & Skills
Strong understanding of Linux operating systems (RHEL, Rocky Linux, Ubuntu, CentOS). Hands-on knowledge of Kubernetes platforms and container orchestration. Good understanding of AI/ML infrastructure components including GPU clusters, model serving platforms, and AI development environments. Familiarity with NVIDIA GPU technologies (H100, H200, L40S, A100, etc.) and GPU resource management. Knowledge of virtualization, networking, storage, and cloud infrastructure. Experience with Kubernetes ecosystem tools such as Helm, Ingress Controllers, Monitoring, and Logging platforms. Understanding of Infrastructure as Code (IaC), automation, and CI/CD practices. Strong troubleshooting skills across Linux, Kubernetes, Storage, Network, and GPU environments. Project management, stakeholder management, and risk assessment capabilities. Excellent customer communication, reporting, documentation, and presentation skills. Capacity planning and resource utilization management experience. Ability to collaborate with cross-functional teams in complex technical deployments.
Key Responsibilities
Manage initiation, planning, scheduling, execution, tracking, and closure of Linux, Kubernetes, AI, and GPU infrastructure projects using standard delivery frameworks. Engage with customers to understand technical and business requirements and translate them into deployment plans and implementation schedules. Work closely with Engineering, Product, and Operations teams to evaluate solution feasibility, architecture readiness, and implementation dependencies. Develop and maintain project plans, milestones, resource allocation, and progress reporting mechanisms. Coordinate deployment of Linux servers, Kubernetes clusters, GPU nodes, AI platforms, and associated infrastructure components. Manage installation, configuration, and integration activities related to: Linux Operating Systems Kubernetes Platforms GPU Infrastructure AI/ML Platforms Networking and Storage Services Monitoring and Security Components Drive customer onboarding and implementation activities for GPU-as-a-Service (GPUaaS) and AI platform offerings. Ensure successful deployment and validation of GPU resources, AI workloads, Kubernetes environments, and platform services. Identify project risks, technical challenges, resource constraints, and dependencies; define and track mitigation plans. Conduct regular governance reviews with customers, vendors, engineering teams, and stakeholders to monitor delivery progress and address escalations. Coordinate platform integration, testing, acceptance validation, and operational handover activities. Monitor project budgets, resource utilization, and delivery timelines to ensure successful project outcomes. Drive service readiness reviews and ensure adherence to operational, security, and compliance requirements. Support troubleshooting and resolution of complex deployment issues involving Linux, Kubernetes, networking, storage, and GPU infrastructure. Identify opportunities for automation, standardization, and continuous improvement in deployment and operational processes. Ensure timely response and closure of customer queries, implementation issues, and project-related escalations. Contribute to enhancement of AI, cloud-native, and GPU platform delivery methodologies, best practices, and operational frameworks. Ensure projects are delivered on time, within budget, and meet customer expectations for performance, scalability, and reliability.