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 of 20% 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.
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