AI architect

AI Architect

The Role

The AI Architect is responsible for defining, designing, and governing endtoend artificial intelligence system architectures that align with business objectives, data strategies, and enterprise technology standards. This role provides technical leadership across AI solution lifecycles, from ideation to production, ensuring scalability, security, interoperability, and regulatory compliance.

Competency Focus: AI Infrastructure and architecture design, cloud-native architecture, model governance, largescale distributed systems

Keywords: HPC Architect, HPC Architecture and System Design

Responsibilities:

• Architect, deploy, and operate large-scale accelerator clusters, including NVIDIA DGX platforms, discrete NVIDIA and AMD GPUs, and TPU-based systems, ensuring high availability, scalability, and performance.
• Design and architect highbandwidth, lowlatency interconnect architectures, leveraging technologies such as InfiniBand, NVLink, and RoCE to support distributed AI training and inference workloads.
• Architect and design endtoend AI training and inference platforms across onpremises and public cloud environments (Azure, AWS, GCP), incorporating elastic GPU resource orchestration and automated scaling mechanisms.
• Architect and engineer highperformance, largescale data delivery and storage solutions, including petabytescale object storage and distributed file systems (e.g., VAST Data, WekaIO, DDN) optimized for AI and highthroughput workloads.
• Design and architect streaming and batch data ingestion pipelines optimized for AI/ML workflows, enabling efficient data preprocessing, feature ingestion, and model training at scale.
• Architect and enforce secure GPU and compute isolation mechanisms, utilizing Kubernetes primitives such as RBAC, namespace isolation, and network policies to ensure multitenant security, governance, and compliance.
• Evaluate, benchmark, and qualify emerging AI hardware platforms and software frameworks, conducting performance, scalability, and costefficiency assessments to inform technology adoption decisions.
• Mentor engineers in AI infra best practices, observability, and capacity management Define the reference architecture for enterprise-wide AI adoption. Understanding on Sovereign AI

Qualifications & Experience

• B. Tech/B.E. in Computer Science, Artificial Intelligence, Data Science, or related discipline; M. Tech/MS preferred
• 12+ years in infrastructure/cloud engineering, with 4+ years focused purely on AI/ML systems.
• Deep expertise in GPU cluster management, distributed compute, and container orchestration.
• Hands-on experience with Kubernetes for AI workloads, GPU scheduling, and Ray/Kubeflow pipelines.
• Basic Understanding of LLM training, fine-tuning, quantization, and model optimization.

Certifications Required:

• NVIDIA Certified Associate AI Infrastructure
• NVIDIA Professional Certification for AI Networking and AI Infrastructure
• Certified Kubernetes Administrator
• Cloud Certification (AWS, Azure, GCP)