Remote Lead Engineer – Agentic AI Infra & Edge

The Wendy's Company; Dublin (OH); Remote; USD 122,000 - 214,000; Full time

Benefits offered by this job

Parental leave

EAP sessions

401k match

Job summary

Wendy's seeks a Lead Engineer – AgenticAI Systems to define the enterprise technical strategy, hardware roadmaps, and deployment architectures for AI compute platforms and edge fleets. You will lead the design of scalable model serving, MLOps/LLMOps, and secure, cost-efficient infrastructure powering store operations and analytics.

You will guide cross-functional teams, establish standards for reliability, and drive multi-region cloud infrastructure initiatives with Kubernetes/GKE deployments.

Qualifications

• Bachelor's Degree in Computer Engineering, Electrical Engineering, or Computer Science is required.
• 5+ years of systems engineering, cloud architecture, and infrastructure leadership.
• Master's degree in computer science or a related field is preferred.
• Experience architecting multi-region cloud infrastructures, hybrid edge-cloud networks, and large-scale Kubernetes/GKE deployment fleets.
• Proficiency with Python, Kubernetes/containerization tools, and cloud technologies (GCP, Azure, AWS).

Responsibilities

• Define enterprise technical strategy, hardware roadmaps, and deployment architectures for distributed AI compute infrastructure, cloud platforms, and store-level edge fleets.
• Establish enterprise system standards for reliability, disaster recovery, hardware security, and compute cost governance.
• Lead architectural spikes, capacity planning, and system benchmarking for next-generation AI workloads and edge inference.
• Partner with Enterprise IT, Security and cross-function teams to design secure networks and gateways.
• Drive technical evaluations, vendor selection, and SLA negotiations for cloud infra providers and edge hardware.

Job description

Wendy's seeks a Lead Engineer – AgenticAI Systems to define the enterprise technical strategy, hardware roadmaps, and deployment architectures for AI compute platforms and edge fleets. You will lead the design of scalable model serving, MLOps/LLMOps, and secure, cost-efficient infrastructure powering store operations and analytics.

You will guide cross-functional teams, establish standards for reliability, and drive multi-region cloud infrastructure initiatives with Kubernetes/GKE deployments.