Hybrid Cloud Presales Consultant

Hybrid Cloud & Public Cloud Pre-Sales Consultant Location: Noida, Bengaluru, Chennai, Pune Experience: 3–12 Years Role Summary Support hybrid cloud pre-sales engagements by designing solutions across on-prem, private, and public cloud environments . Work closely with sales and delivery teams to build customer-centric solutions. Key Responsibilities Support technical presales engagements for datacenter, hybrid cloud, private cloud, public cloud, automation, operations and AI infrastructure opportunities. Participate in discovery discussions and customer workshops to understand current-state infrastructure, pain points, transformation objectives, business priorities and budget considerations. Translate customer requirements into solution architectures covering compute, storage, backup, network, virtualization, security, automation, observability and cloud operations. Develop solution proposals, high-level architectures, BoMs, sizing assumptions, SoW inputs, migration approaches and implementation assumptions. Support RFP/RFI responses with clear technical, operational and commercial inputs. Prepare and deliver customer presentations, solution walkthroughs, whiteboarding sessions, demos and PoCs. Collaborate with OEMs and technology partners. Position infrastructure modernization solutions and cloud operating models. Contribute to automation and operations transformation solutions covering SRE, AIOps, observability, ITSM integration, infrastructure as code and runbook automation. Support AI Factory and AI-ready infrastructure solutioning across GPU compute, high-performance networking, storage, Kubernetes/container platforms, MLOps/LLMOps and operational governance. Explain Agentic AI use cases for IT and cloud operations, such as intelligent ticket triage, root cause analysis, autonomous remediation, autonomous Finops, knowledge assistants, change-risk insights and workflow agents. Maintain reusable presales assets including proposal templates, architecture patterns, demo narratives, reference BoMs and competitive positioning material. Required Skills Presales and solutioning: Experience in IT infrastructure, datacenter, cloud, automation, operations or transformation presales; ability to convert requirements into solution outcomes. Datacenter foundation: Understanding of servers, storage, backup, network, virtualization, operating systems, databases, disaster recovery, security and enterprise availability requirements. Private cloud and SDDC: Exposure to VMware VCF/vSAN/Aria, Nutanix, Azure Stack HCI, OpenStack, Red Hat OpenShift virtualization or similar private cloud/SDDC platforms. Public cloud: Working understanding of AWS, Microsoft Azure and/or Google Cloud; ability to discuss landing zones, migration, connectivity, identity, backup, security, resiliency and cost/performance considerations. Hybrid cloud architecture: Ability to position hybrid operating models, workload placement, migration waves, cloud management, governance, cost optimization and operational runbooks. Automation: Exposure to Ansible, Terraform, PowerShell/Python scripting, CloudFormation/Bicep, GitOps, CI/CD or equivalent automation tooling. Operations transformation: Understanding of ITIL/ITSM, SRE, observability, monitoring, logging, tracing, event management, CMDB integration, AIOps and product-driven delivery models. Proposal development: Ability to create solution notes, architecture diagrams, BoM inputs, SoW assumptions, RFP/RFI responses, effort assumptions and risk/constraint statements. Customer communication: Strong listening, presentation, storytelling and documentation skills; ability to articulate customer pain points and map them to practical technology and commercial solutions. Understanding of AI Factory concepts : Full-stack AI Factory infrastructure designed to build, train, tune, deploy and operate AI workloads at enterprise scale. AI Factory Building Blocks : Exposure to AI-ready infrastructure building blocks. Awareness of MLOps/LLMOps concepts including data pipelines, model lifecycle, model registry, inference endpoints, prompt/model evaluation, monitoring and governance. Understanding of GPU virtualization , workload scheduling, data locality, storage throughput, network latency and resiliency considerations for AI workloads. Agentic AI : Ability to translate IT operations problems into AI assistant and agent workflows, such as incident triage, root cause analysis, remediation, capacity insights, compliance checks, FinOps and knowledge automation. Hands-on PoC or demo experience with GenAI or Agentic AI is preferred. Nice to Have Experience in pre-sales or solution architecture Awareness of multi-hypervisor environments Basic understanding of security and cloud governance