Senior Agentic AI Systems Engineer – Runtime Frameworks & Resource Intelligence

Houston, TXfull timeOn Site

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senior

$154k–$228k

H1B: yes

Hp Senior Agentic AI Systems Engineer focused on runtime frameworks and resource intelligence for agentic AI across PC and edge computing environments. Responsible for analyzing, tailoring, and integrating AI frameworks to manage concurrent agents under dynamic system conditions.

Responsibilities

Analyze agentic AI frameworks and runtime architectures for deployment across PC and edge computing environments.
Tailor and integrate agentic frameworks to operate effectively under changing resource and system conditions.
Evaluate how multiple concurrent agents, AI models, and applications use and compete for compute, memory, storage, networking, power, and thermal resources.
Develop policies for workload admission, prioritization, scheduling, orchestration, throttling, suspension, recovery, and termination.
Create capabilities that maintain application and system responsiveness while multiple agentic workloads execute concurrently.
Develop graceful-degradation strategies for memory pressure, compute contention, low battery, thermal limits, intermittent connectivity, and other constrained conditions.
Analyze the system impact of agent planning, tool execution, retrieval, model inference, context management, state persistence, and inter-agent communication.
Define approaches for managing agent context, memory, state, shared resources, and long-running sessions.
Develop abstractions that support different AI models, runtimes, hardware configurations, and local, edge, or cloud-connected execution environments.
Create workload characterization, benchmarking, stress-testing, and validation methods for multi-agent scenarios.
Instrument agentic runtimes to collect telemetry related to performance, resource consumption, responsiveness, reliability, and execution outcomes.
Diagnose complex issues spanning agentic frameworks, AI runtimes, operating systems, applications, services, and hardware resources.

Qualification

Experience building telemetry

Preferred

Experience deploying agentic AI frameworks on PCs, edge devices, embedded platforms, or other resource-constrained systems.
Experience adapting open-source or commercial agentic frameworks for production environments.
Experience developing resource-aware runtimes, schedulers, orchestration systems, policy engines, or workload-management services.
Familiarity with operating-system scheduling, power management, thermal management, memory management, or resource-governance technologies.
Experience with long-running AI workflows, context management, agent memory, retrieval-augmented generation, or shared model services.
Familiarity with unified-memory architectures and shared-memory accelerator designs.
Experience building telemetry, observability, performance-analysis, or system-health capabilities.
Experience collaborating with silicon providers, operating-system vendors, cloud providers, or AI ecosystem partners.