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Develops technical content, interactive demonstrations, reference architectures, and enablement tools that showcase AI infrastructure and data platforms. Partners with product, marketing, engineering, sales, and partners to translate complex AI/ML architectures, RAG workflows, storage, and networking technologies into compelling technical narratives. Presents at conferences and executive briefings, leads hands-on technical showcases, supports enterprise deals, and channels customer feedback into product and developer-experience improvements.

Design, build, and scale CI/CD, build, and test pipelines supporting Pure Storage engineering teams. Develop production services, APIs, workflows, and developer tools; improve architecture, performance, capacity, reliability, observability, and diagnostics. Partner with product engineering, release engineering, and platform teams to solve workflow problems and support releases. Own testing, documentation, operational readiness, incident follow-through, and long-term system improvements.

Lead end-to-end product ownership for data management and reporting products: customer discovery, requirements, prioritization, and post-launch iteration. Collaborate with engineering, design, GTM, and customers to build in-product reports and exports for third-party BI/datalake tools, drive roadmap planning, and represent product in technical and customer-facing meetings.

Leads architecture, roadmap, design reviews, validation, bring-up, diagnostics, and failure analysis for enterprise and cloud-scale storage hardware. Establishes server, rack-scale power and thermal strategies, validation frameworks, automated testing, and reliability standards. Coordinates hardware, firmware, software, executive, and OEM/ODM/JDM teams while mentoring engineers and driving complex system-level issue resolution.

Build and scale an enterprise Retrieval-Augmented Generation platform using Go and Python. Develop full-stack features, backend microservices, frontend interfaces, and AI/ML integrations in partnership with NVIDIA engineering teams. Own features from concept through production, while improving system performance, testing, security, availability, and reliability. Apply hands-on expertise in RAG, LLM pipelines, and AI-driven data retrieval architectures to deliver low-latency enterprise capabilities.

Architects enterprise data migration, disaster recovery, hybrid-cloud storage, and high-availability infrastructure solutions. Partners with Sales, Engineering, and enterprise customers on pre-sales strategy and post-sales delivery. Leads complex data center consolidations, block and file migrations, automation scripting, replication, cloud integration, technical scoping, risk management, and project execution.

Lead the strategy, team, platforms, and processes supporting PCBA- and system-level manufacturing testing. Build scalable test frameworks, validation automation, infrastructure, testbooks, equipment configurations, CI/CD validation, release governance, and test software verification. Establish reliable manufacturing test data foundations, APIs, databases, observability, and operational dashboards while partnering with engineering, manufacturing, quality, and contract manufacturers to improve throughput and product quality.

Leads global manufacturing engineering for server and storage products across PCBA and system-level manufacturing. Responsibilities include process strategy and validation, NPI ownership, DFM, PFMEA, automation, mistake-proofing, continuous improvement, technology roadmaps, quality and yield improvement, and team development. Partners with design engineering, quality, operations, and contract manufacturers to establish scalable, cost-effective, high-throughput production systems.

Designs and delivers scalable Java backend platform capabilities for discovering, classifying, and protecting sensitive data. Responsibilities include distributed-system development, integrations with data sources and storage systems, performance and concurrency optimization, testing, deployment, troubleshooting, code reviews, and architectural improvement. The role collaborates with engineering, product, and customer-facing teams and requires working primarily in the Prague office.

Design, develop, test, and operate high-throughput distributed data processing services connecting edge devices with the cloud platform. Collaborate with product managers on scalable architecture, improve performance, security, and cost efficiency, and maintain reliable CI/CD automation. Resolve complex integration issues across legacy and modern components while ensuring high availability. The role requires strong Java or Kotlin backend expertise, cloud and Kubernetes experience, distributed systems knowledge, Linux proficiency, and basic networking fundamentals.