HP
Director - Agentic AI Computing Foundation
- Compensation
- Not disclosed
- Location
- Spring, Texas, United States of America
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Contract
- Experience
- 7+ years
- Category
- Not specified
- Posted
Skills
Must have
- Typically 15 or more years in systems, embedded, device-platform, or AI engineering, including at least 7 years leading teams and experience managing managers and senior architects.
- A record of delivering production capabilities integrated into PCs or comparable edge devices, including responsibility for runtime reliability, OS or hardware interfaces, and field behavior.
- Deep understanding of heterogeneous compute and on-device inference, including CPU, GPU, NPU, memory hierarchy, performance profiling, and power or thermal constraints.
- Strong systems architecture experience involving native software, concurrency, interprocess communication, API compatibility, device services, and secure lifecycle management.
- Practical experience enabling agentic or generative AI systems, with understanding of orchestration, tool permissions, model/runtime integration, observability, and evaluation.
- Ability to lead global technical organizations and partner engineering. Degree in computer science, electrical or computer engineering, or a related discipline, or equivalent relevant experience.
Nice to have
- Depth in Windows device platforms and familiarity with supported Linux or embedded environments, firmware interfaces, silicon SDKs, and hardware-in-the-loop testing.
- Experience with model compression, quantization, inference acceleration, local context stores, and policy-based placement across device and cloud.
- Experience with OEM or ODM development, enterprise manageability, protocol interoperability, and platform adoption by internal developers or ISVs.
Full description
Director - Agentic AI Computing Foundation
Description -
Role purpose
Lead the engineering organization that builds the shared backbone for executable agentic AI on HP PCs and edge devices. Own platform capabilities spanning APIs, security, hardware interfaces, data access, and policy frameworks. Make agentic features portable, reliable, and efficient across supported device configurations while providing clear technical contracts to feature teams.
Our organization advances HP AI Roadmap through capabilities deeply integrated into HP PCs and edge devices.
Key responsibilities
- Platform architecture. Set the foundation roadmap for context, identity, capability discovery, orchestration, and manageability. Define stable APIs and versioned contracts that multiple experiences can reuse.
- Device integration. Lead collaboration across runtime, OS services, drivers, firmware, and hardware interfaces. Establish lifecycle behavior for startup, sleep and resume, connectivity changes, updates, and recovery.
- Execution efficiency. Guide inference and workload placement across CPU, GPU, NPU, and cloud resources. Set performance budgets for latency, memory, power, and thermals with hardware and silicon partners.
- Security and control. Provide trusted identity, permission boundaries, policy enforcement, protected data access, and auditable agent actions. Support safe tool invocation, user control, and enterprise endpoint management.
- Engineering partnerships. Work with Vali’s team and other engineering stakeholders to make features executable. Integrate capabilities from internal teams and relevant industry protocols through tested adapters and contracts.
- Continuous enablement. Commit Foundation engineers to agile squads from discovery through adoption. Deliver reusable increments, compatibility tests, telemetry, and reliability improvements alongside feature development.
- Organization leadership. Build a global systems and AI engineering leadership team. Own platform investment, technical talent development, capacity, and technical debt while balancing shared needs across Divisions.
Required qualifications
- Typically 15 or more years in systems, embedded, device-platform, or AI engineering, including at least 7 years leading teams and experience managing managers and senior architects.
- A record of delivering production capabilities integrated into PCs or comparable edge devices, including responsibility for runtime reliability, OS or hardware interfaces, and field behavior.
- Deep understanding of heterogeneous compute and on-device inference, including CPU, GPU, NPU, memory hierarchy, performance profiling, and power or thermal constraints.