Artificial Intelligence Architect

Key Responsibilities

Owns the full lifecycle of AI solutioning—from discovery to deployment—driving business impact through scalable, customer-centric AI architectures.

• Engage customers to identify and shape AI use cases aligned to business goals
• Translate requirements into scalable end-to-end AI architectures
• Lead pre-sales activities (solutioning, demos, POCs, RFPs)
• Act as trusted technical advisor to CXOs and stakeholders
• Design and position AI-driven solutions (GenAI, analytics, automation)
• Drive rapid prototyping and innovation (POC → MVP → scale)
• Integrate AI solutions with enterprise systems and data platforms
• Ensure security, governance, and Responsible AI compliance
• Collaborate with partners, ISVs, and internal teams for delivery
• Bridge pre-sales to implementation ensuring successful deployment
• Define and track business value, ROI, and outcomes
• Evangelize AI through executive presentations, workshops, and thought leadership
• Design end-to-end AI architectures leveraging NVIDIA AI Enterprise and GPU-accelerated infrastructure
• Architect real-time video analytics pipelines using NVIDIA Metropolis and DeepStream
• Implement and customize NVIDIA Blueprints for production-ready deployments
• Integrate NVIDIA Video Search and Summarization (VSS) into operational command center workflows
• Design scalable edge-to-cloud AI frameworks optimized for live venue environments
• Architect digital twin and simulation environments leveraging NVIDIA Omniverse
• Define multimodal AI architectures combining computer vision, speech AI, and generative AI workflows
• Support real-time avatar and digital human applications powered by NVIDIA ACE and related technologies
• Establish performance, scalability, and governance standards for AI deployments
• Collaborate cross-functionally with engineering, infrastructure, and product teams

Required Qualifications

• Bachelor's Degree in Computer Science or related field
• 8+ years of experience in AI/ML architecture, distributed systems, or enterprise AI platforms
• Strong hands-on experience with:
  • NVIDIA AI Enterprise
  • NVIDIA Metropolis and DeepStream
  • NVIDIA Blueprints
  • CUDA and GPU optimization
• Experience designing real-time computer vision and video analytics systems
• Experience building edge-to-cloud AI architectures
• Expertise in containerized AI workloads (Docker, Kubernetes)
• Strong understanding of high-performance computing environments

Preferred Qualifications

• Experience with NVIDIA Video Search and Summarization (VSS)
• Experience with NVIDIA Omniverse and digital twin frameworks
• Experience with NVIDIA ACE (Avatar Cloud Engine) or real-time digital human workflows
• Familiarity with multimodal AI systems (vision + speech + generative AI)
• Experience in sports venues, smart infrastructure, or real-time operational environments