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In the HPE Hybrid Cloud, we lead the innovation agenda and technology roadmap for all of HPE. This includes managing the design, development, and product portfolio of our next-generation cloud platform, Green Lake. Working with customers, we help them reimagine their information technology needs to deliver a simple, consumable solution that helps them drive their business results. Join us redefine what's next for you.
Job Responsibility
Serve as the premier technical authority bridging advanced machine learning, GenAI, and big data analytics with HPE's next-generation cloud and storage ecosystem (HPE GreenLake and hybrid cloud)
Lead the architectural strategy to extract, analyze, and operationalize intelligence from vast telemetry data pipelines generated by enterprise file, block, and object storage systems
Define the organization-wide data architecture strategy and analytical roadmaps for software systems running across HPE's hybrid cloud platform
Develop predictive, prescriptive, and generative AI models to map data paths, enhance memory/space management, and predict capacity or hardware failure across global enterprise clusters
Validate highly complex, distributed telemetry data from customer storage networks to uncover structured insights that drive automated mitigation and system reliability
Design, deploy, and scale machine learning and deep learning code to run reliably in worldwide production edge-to-cloud environments
Collaborate with data engineering to establish standardized ELT patterns, big data storage views, and high-throughput, low-latency streaming pipelines (supporting Kafka, Spark, etc.)
Integrate advanced LLM workflows, Retrieval-Augmented Generation (RAG), and agentic systems into storage customer support analytics and digital products to automate troubleshooting
Partner with product management, storage hardware engineers, and business executives to translate abstract business challenges into production-ready analytical solutions
Provide career guidance, conduct design reviews, and actively mentor senior engineers and data scientists across multiple Scrum teams to foster an innovative technical community
Requirements
10+ years of proven industry experience in software product development or enterprise data science, with a heavy emphasis on distributed systems, storage, or cloud infrastructure architectures
Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Mathematics, or a highly technical, data-oriented discipline
Advanced mastery of machine learning algorithms (time-series forecasting, clustering, anomaly detection, random forests) and deep learning frameworks
Expert Python/Go-lang programmer
Strong working knowledge of data structures, algorithmic complexity, and multi-threaded programming
Experience with C/C++ or system internals is a strong differentiator
Concrete experience building and fine-tuning LLMs, prompt engineering, and leveraging vector databases
Exceptional ability to explain highly technical architectural trade-offs, algorithms, and AI solutions clearly to non-technical business leaders and executive management
A proven track record of industry innovation, backed by whitepapers, industry conference contributions, or patents in software and analytical design