Director Unstructured Presales Storage Leader

Director Unstructured Presales Storage Leader This role has been designed as 'Hybrid' with a requirement that you will work on average 2 days per week from an HPE office. Who We Are: Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work. We help companies connect, protect, analyze, and act on their data and applications wherever they live, from edge to cloud, so they can turn insights into outcomes at the speed required to thrive in today’s complex world. Our culture thrives on finding new and better ways to accelerate what’s next. We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good. If you are looking to stretch and grow your career our culture will embrace you. Open up opportunities with HPE. Job Description: We are seeking a strategic and technically grounded Pre-Sales Leader to drive the next phase of growth for our X10K AI data platform business. This leader will own the pre-sales strategy, execution, and enablement required to accelerate customer adoption of AI Factory solutions across RAG, inference, and model training workloads. The role combines customer engagement, technical leadership, sales execution, team development, and cross-functional influence. You will partner closely with enterprise customers, account teams, solution architects, partners, and Product Management to shape high-value opportunities, develop compelling solution architectures, and build repeatable go-to-market motions. A data-first perspective is essential. You will guide customers in understanding how data is created, moved, enriched, accessed, and consumed across AI pipelines, positioning infrastructure and data platforms as enablers of business value rather than as the starting point of the conversation. The ideal candidate brings strong commercial judgment, deep understanding of AI and data architectures, and the ability to lead teams through complex enterprise sales cycles across APAC and EMEA. You will help identify the right opportunities at the right time, align solutions to customer readiness and workload requirements, and ensure we win where we can deliver sustainable, measurable impact. This is not a pure storage role. However, a strong understanding of how modern data platforms and storage technologies enable AI pipelines is essential. Key Responsibilities Pre-Sales Strategy and Leadership • Define and execute the pre-sales strategy for the X10K AI data platform business. • Partner with sales leadership to develop account strategies, territory plans, pipeline priorities, and opportunity qualification criteria. • Establish repeatable approaches for identifying, shaping, qualifying, and advancing AI Factory opportunities. • Apply strong technical and commercial judgment to prioritize opportunities based on customer readiness, workload fit, scale, competitive position, and likelihood of long-term success. • Drive consistency in pre-sales execution, forecasting, deal inspection, technical validation, and executive engagement. • Establish and track measures such as pipeline contribution, deal velocity, win rate, solution adoption, and customer outcomes. Customer Engagement and Deal Leadership • Lead strategic customer engagements with CTOs, CIOs, Heads of AI, Data Engineering leaders, and other executive stakeholders. • Direct technical discovery sessions to understand business objectives, data characteristics, AI workloads, existing environments, and desired outcomes. • Translate customer requirements into scalable AI architectures and compelling business-aligned solution designs. • Guide pre-sales teams through complex, multi-stakeholder enterprise sales cycles. • Serve as an executive-level technical advisor throughout the opportunity lifecycle. • Align technical solutions to measurable business outcomes, including performance, cost efficiency, reliability, scalability, and time to value. • Help sales teams position solutions with precision and credibility, ensuring that proposed architectures are fit for purpose. Data-Centric AI Architecture • Lead architecture discussions based on data requirements and lifecycle considerations, including: • Data volume, velocity, variety, and distribution • Structured and unstructured data • Data locality, gravity, and movement patterns • Data access, governance, metadata, and retrieval requirements • Ensure solution designs optimize: • Sequential versus random access patterns • Batch versus real-time processing • Data movement between storage, compute, and model layers • Metadata, indexing, and retrieval efficiency for RAG • Performance, cost, reliability, and trustworthiness • Evaluate how data architecture decisions affect: • Model performance and accuracy • Latency and time-to-first-token • GPU utilization • Data ingestion and preparation requirements • Overall operating cost and scalability AI Solution Architecture, Sizing, and Validation • Lead the development and validation of AI Factory architectures for: • Retrieval-Augmented Generation • Model inference and deployment • Model training and fine-tuning • Guide teams in sizing environments based on: • GPU counts and configurations • Data volumes and throughput requirements • Model types and workload characteristics • Ingestion, retrieval, and serving requirements • Define performance expectations across the full AI pipeline. • Establish methodologies for evaluating time-to-first-token, throughput, GPU efficiency, data access performance, and cost. • Ensure proposed designs are technically sound, commercially viable, and aligned with customer requirements. • Support proof-of-concept, benchmark, and technical validation activities where required. X10K Data Platform Positioning • Define and articulate the strategic value of X10K within modern AI architectures. • Position data platforms as crit…