Lead Engineer - Agentic AI Systems

Accountabilities

• Define the enterprise technical strategy, hardware roadmaps, and deployment architectures for distributed AI compute infrastructure, cloud platforms, and store-level edge environments.

• Establish technical standards for infrastructure reliability, disaster recovery, hardware security, and compute cost governance across AI platforms and deployments.

• Lead architectural investigations, capacity planning exercises, and system benchmarking for next-generation AI workloads, multimodal models, and low-latency edge inference.

• Design scalable model-serving, MLOps, and LLMOps infrastructure that enables AI, machine learning, and agentic solutions to operate reliably across enterprise environments.

• Partner with Enterprise IT, Security, cross-functional stakeholders, and hardware vendors to develop network topologies, edge hardware specifications, secure API gateways, and deployment architectures.

• Evaluate cloud infrastructure providers, GPU vendors, and edge hardware manufacturers, supporting vendor selection and negotiating technical requirements and service-level agreements.

• Drive infrastructure right-sizing, security improvements, performance optimization, and cost efficiency across AI compute environments.

• Provide technical oversight and architectural review across infrastructure and AI engineering initiatives, ensuring consistency with enterprise standards and long-term strategy.

• Establish scalable frameworks, engineering best practices, and technology standards for the development and operation of AI platforms.

• Lead and mentor cross-functional engineering teams, set technical direction, and guide delivery across multiple concurrent strategic initiatives.

Requirements:

• A Bachelor’s degree in Computer Engineering, Electrical Engineering, Computer Science, or a related field, combined with 5+ years of experience in systems engineering, cloud architecture, and infrastructure leadership.

• A Master’s degree in Computer Science or a related discipline is preferred.

• Demonstrated experience architecting multi-region cloud infrastructures, hybrid edge-cloud networks, and large-scale Kubernetes or GKE deployment fleets.

• A strong track record of leading complex engineering initiatives, establishing technical vision, and influencing significant technology investments and vendor decisions.

• Deep knowledge of heterogeneous compute environments, including GPU, NPU, and TPU architectures, as well as low-latency networking, distributed storage, and inference acceleration runtimes.

• Proficiency with Python, Kubernetes and containerization technologies, along with major cloud platforms such as GCP, Azure, or AWS.

• Strong understanding of scalable AI infrastructure, model serving, MLOps/LLMOps, and the operational requirements of modern AI and agentic systems.

• Excellent architectural, analytical, and problem-solving abilities, with the capacity to balance performance, reliability, security, scalability, and cost.

• Strong leadership and communication skills, with experience providing technical direction, mentoring engineers, and collaborating across infrastructure, security, IT, AI, and external vendor teams.

• Previous experience in the food service or a related industry is preferred.

Benefits:

• Base salary ranging from$122,000 to $214,000 per year, depending on factors such as experience, skills, knowledge, internal equity, and business considerations.

• Target annual bonus of20% of annualized base salary, based on company and individual performance.

• Primarily remote work arrangement, with travel to designated company locations or other sites as business needs require.

• Company 401(k) match.

• Parental leave.

• Access to Employee Assistance Program (EAP) sessions.

• Eligibility for additional benefits and incentives offered through the company’s applicable benefit plans and policies.