Distinguished Technologist, Edge AI Architect

HP is leading the way in Edge AI, building the next era of secure, mobile, and local AI solutions. This role provides senior technical leadership and end-to-end architectural oversight of a full-stack Edge AI platform, spanning from silicon and systems hardware to model management and lifecycle governance.

About the role

As the highest-level individual technical authority for the platform, you will set and own the technical direction across every layer of the stack — hardware enablement, security and OS trust foundation, inference serving, agentic runtimes, fleet management, and model management. The role ensures the platform behaves as one coherent, secure, and performant system rather than a collection of independent components. You will evaluate and introduce technologies, define cross-layer architecture and interface contracts, and establish engineering standards and best practices.

Working closely with product managers, engineering leaders, firmware and hardware teams, security, quality assurance, and business stakeholders, you will gather requirements, define architectural scope, and drive alignment throughout the full development lifecycle — from on-device silicon enablement to model lifecycle governance and edge-to-cloud orchestration.

Responsibilities

• Own the multi-year technical roadmap and architectural vision for a full-stack Edge AI platform, focusing on orchestrating LLMs, vLMs, and agent-based systems across constrained, on-device, and clustered environments.
• Define cross-layer architecture and interface contracts connecting hardware, OS/security, inference, agentic runtime, and model-management layers to ensure the platform operates as a single, coherent, and upgradeable system.
• Architect model management, registry, and lifecycle systems, governing versioning, signing, evaluation, promotion, provenance, and rollback of models across a distributed fleet.
• Set the architecture for agentic AI runtimes and agent-creation tooling, defining how agents are built, sandboxed, permissioned, tool-integrated, governed, and safely operated in production.
• Drive the inference-serving strategy, including model serving, inference gateways, and model/request routing, optimized for throughput, latency, and cost across heterogeneous silicon.
• Architect the control plane, end-to-end telemetry, and cost-management frameworks to make on-device and clustered deployments deployable, observable, and manageable at scale.
• Own the security architecture, including hardware-rooted chain of trust, secure boot, workload isolation, and sandboxing, ensuring safe execution of agentic workloads.
• Partner with silicon, firmware, and hardware teams to exploit modern compute platforms and build abstraction layers enabling AI workloads to deploy across diverse silicon without rewrites.
• Architect seamless edge-to-cloud handoff frameworks optimized for cost, latency, privacy, and performance, defining when and how workloads run on-device, at the cluster, or in the cloud.
• Enable multi-modal AI experiences, integrating vision, audio, and text inputs from the runtime through to model management.
• Design scalable on-device lifecycle management frameworks, including deployment, observability, updateability, and manageability, that hold up across a distributed fleet.
• Drive cross-functional influence, bringing together experts across software, firmware, hardware, security, and business teams to converge on a unified platform architecture.
• Communicate technology strategy and the multi-year roadmap to executive leadership, industry partners, and customers, translating deep technical direction into business impact.
• Serve as a trusted technical advisor and the enterprise's top design authority for Edge AI, influencing enterprise-level decision-making through combined technical and business expertise.
• Provide architectural guidance, consultation, and design-review authority across all layers, applications, and platforms, resolving cross-layer trade-offs and setting engineering standards.
• Assess emerging technologies, develop business cases, and shape the platform portfolio in partnership with architects, product leaders, and operations.
• Ensure effective enablement and training for engineering, services, support, and sales teams.
• Mentor and develop emerging technical leaders and architects, fostering a culture of innovation and engineering excellence.

Requirements

• Four-year or Graduate Degree in Computer Science, Information Technology, Software Engineering, or a related discipline, or commensurate work experience or demonstrated competence.
• Typically 12+ years of work experience, preferably in software design & development, software architecture, programming languages, or a related field.
• Demonstrated experience architecting across multiple layers of a modern AI stack — from hardware/OS enablement and inference serving to agentic runtimes and model lifecycle.

Skills

• LLM, vLM, and multi-modal model architecture and orchestration
• Agentic AI systems and runtimes (agent harnesses, tool use, sandboxing, governance)
• Inference serving and optimization (model serving, inference gateways, model/request routing)
• Edge AI and edge-to-cloud architecture (latency, cost, privacy, on-device constraints)
• Model management, registry, lifecycle, versioning, and provenance
• GPU/accelerator computing and heterogeneous silicon (CUDA and related)
• Hardware/software co-design and silicon abstraction layers
• Security foundations: chain of trust, secure boot, isolation, sandboxing, and confidential computing
• Fleet management, control planes, observability, and telemetry
• Distributed systems and scalability
• Kubernetes, Docker, and containerized/microservices architecture
• Python, C++, Rust (systems-level and ML tooling)
• MLOps / LLMOps and CI/CD for models and agents
• Cloud platforms (AWS, Microsoft Azure) and hybrid deployment
• Cost, latency, and performance optimization at scale
• DevOps and automation
• Software engineering and full-stack development
• APIs and interface/contract design across layers
• Effective communication and cross-organizational collaboration
• Results orientation and learning agility
• Customer centricity and impact-driven mindset

Preferred Certifications

• Programming Language Certification (Python, C++, Rust, Java, or similar)
• Cloud or platform architecture certification (AWS, Azure, or CNCF/Kubernetes) is a plus

Pay

The pay range for this role is $174,050 to $278,450 USD annually, with additional opportunities for pay in the form of bonus and/or equity (applies to United States of America candidates only). Pay varies by work location, job-related knowledge, skills, and experience.

Schedule

• Full time
• No shift premium (United States of America)

Benefits

• Health insurance
• Dental insurance
• Vision insurance
• Long-term/short-term disability insurance
• Employee assistance program
• Flexible spending account
• Life insurance
• 4-12 weeks fully paid parental leave based on tenure
• 11 paid holidays
• Additional flexible paid vacation and sick leave (US benefits overview)