AI Infrastructure Solutions Architect

Lead the design of end-to-end AI infrastructure architectures, including GPU data centers, high-performance computing (HPC) clusters, and AI compute environments. Design high-performance networking solutions, GPU interconnects, and scalable infrastructure supporting demanding AI workloads. Assess data center capacity, power, cooling, cabling, and physical infrastructure requirements to ensure scalability, performance, and reliability. Design cloud, private-cloud, on-premises, and hybrid infrastructure architectures for enterprise AI workloads. Define infrastructure requirements for large language models (LLMs), model serving, Retrieval-Augmented Generation (RAG), AI agents, and data-intensive applications. Establish containerized deployment architectures using Docker, Kubernetes, and other relevant orchestration technologies. Oversee DevOps and MLOps architecture, including CI/CD pipelines, infrastructure as code, automated deployments, and production monitoring. Define production-readiness standards covering system reliability, scalability, performance optimization, disaster recovery, and incident management. Establish infrastructure security standards covering identity and access management, network segmentation, encryption, data protection, and workload isolation. Ensure infrastructure architectures comply with enterprise security, data residency, privacy, and regulatory requirements. Collaborate with AI, software, data, cloud, and infrastructure engineering teams to translate application requirements into secure, scalable, and production-ready solutions. Provide technical architecture leadership, guide engineering teams, and oversee infrastructure implementation across multiple business units and technical environments. Lead technical discovery sessions, architecture workshops, and solution feasibility assessments with enterprise, government, data center, and technology customers. Develop technical proposals, reference architectures, bills of materials, RFP/RFQ responses, and proof-of-concept designs. Support business development activities by translating customer requirements into commercially viable AI infrastructure and platform solutions. Build and maintain technical relationships with hardware manufacturers, cloud providers, hyperscalers, and technology partners. Evaluate emerging technologies in GPU infrastructure, AI networking, cloud-native platforms, and AI deployment systems to identify new business opportunities. Develop reusable architecture frameworks, deployment standards, and technical documentation to support consistent delivery across different client environments. Qualifications Bachelor’s degree in Computer Science, Computer Engineering, Information Technology, Network Engineering, or a related technical field. Significant experience in infrastructure architecture, data center solutions, cloud architecture, or enterprise technology environments. Strong understanding of AI/GPU workloads, high-performance computing, compute infrastructure, storage, and high-speed networking. Experience designing and overseeing production cloud, private-cloud, on-premises, or hybrid infrastructure environments. Working knowledge of Docker, Kubernetes, infrastructure as code, CI/CD, DevOps, and production observability. Understanding of production AI infrastructure, including LLM inference, model serving, RAG, AI agents, and MLOps environments. Knowledge of enterprise security architecture, identity management, network security, disaster recovery, and data residency requirements. Experience translating business and technical requirements into scalable, secure, and commercially viable architecture solutions. Strong technical leadership skills, with the ability to guide cross-functional engineering teams and oversee complex infrastructure initiatives. Previous experience in customer-facing solution architecture, technical consulting, presales, or enterprise technology engagements. Excellent communication, stakeholder management, analytical, and problem-solving skills. Ability to balance technical requirements, infrastructure costs, performance, scalability, and business objectives.

Desired Candidate Profile

Qualifications Bachelor s degree in Computer Science, Computer Engineering, Information Technology, Network Engineering, or a related technical field. Significant experience in infrastructure architecture, data center solutions, cloud architecture, or enterprise technology environments. Strong understanding of AI/GPU workloads, high-performance computing, compute infrastructure, storage, and high-speed networking. Experience designing and overseeing production cloud, private-cloud, on-premises, or hybrid infrastructure environments. Working knowledge of Docker, Kubernetes, infrastructure as code, CI/CD, DevOps, and production observability. Understanding of production AI infrastructure, including LLM inference, model serving, RAG, AI agents, and MLOps environments. Knowledge of enterprise security architecture, identity management, network security, disaster recovery, and data residency requirements. Experience translating business and technical requirements into scalable, secure, and commercially viable architecture solutions. Strong technical leadership skills, with the ability to guide cross-functional engineering teams and oversee complex infrastructure initiatives. Previous experience in customer-facing solution architecture, technical consulting, presales, or enterprise technology engagements. Excellent communication, stakeholder management, analytical, and problem-solving skills. Ability to balance technical requirements, infrastructure costs, performance, scalability, and business objectives. Preferred Qualifications Experience designing AI data centers, GPU clusters, or HPC infrastructure. Familiarity with NVIDIA GPU platforms, InfiniBand, high-speed Ethernet, and large-scale AI compute deployments. Experience supporting production LLM inference, AI model serving, or enterprise AI platform deployments. Knowledge of major infrastructure and technology providers, including NVIDIA, AMD, Intel, Dell, HPE, Lenovo, Supermicro, Cisco, Arista, and leading cloud platforms. Experience developing reusable infrastructure architectures and multi-client deployment frameworks. Relevant certifications in cloud architecture, Kubernetes, NVIDIA technologies, data center infrastructure, or enterprise networking. Required Skills CI/CD Cloud Architecture DevOps Infrastructure Azure Cloud Services