Artificial Intelligence Architect

As an experienced AI Storage Architect at our company, you will be responsible for leading the design and development of next-generation AI-powered storage and cloud platforms. Your expertise in Storage Architecture, Data Path Engineering, Distributed Systems, Cloud Platforms, and AI/ML technologies will be crucial in building scalable, high-performance enterprise solutions. Key Responsibilities: - Design, develop, and deploy AI models, including LLMs, Agentic AI systems, NLP models, Computer Vision solutions, and Deep Learning applications. - Build autonomous storage management platforms powered by LLMs and Agentic AI for predictive capacity planning, anomaly detection, performance tuning, and self-healing operations. - Develop AI-powered tools and platforms using Python, R, TensorFlow, PyTorch, and modern AI frameworks. - Define storage-specific use cases for Generative AI and Agentic AI, including intelligent storage operations, automated troubleshooting, capacity optimization, and infrastructure governance. - Design and implement RAG (Retrieval-Augmented Generation) architectures integrating vector databases, storage systems, and enterprise knowledge repositories. - Architect and deploy multi-agent systems across Kubernetes, cloud services, edge environments, and enterprise application platforms. - Define agent lifecycle management, orchestration, memory management, communication protocols, governance, and security controls. - Design scalable architectures capable of supporting thousands of AI agents across multiple clouds and data centers. - Implement observability, monitoring, and governance frameworks for AI agent ecosystems. - Design and architect enterprise-scale storage platforms, cloud-native infrastructure, and distributed systems. - Optimize storage datapaths for high-throughput AI/ML workloads, ensuring performance, scalability, and reliability. - Design and implement stack-level solutions, storage protocols, and data management architectures. - Build scalable solutions leveraging Kubernetes, Elasticsearch, cloud platforms, and distributed computing frameworks. - Drive storage modernization initiatives across hybrid and multi-cloud environments. Required Skills & Experience: - Strong background in Product Engineering and System Design. - Hands-on expertise in Storage Architecture, Data Path, and Stack-Level Programming. - Experience with Kubernetes, Elasticsearch, Distributed Systems, and Cloud Platforms. - Strong expertise in AI/ML algorithms, Deep Learning, NLP, and Computer Vision. - Hands-on experience with AI/ML frameworks such as TensorFlow, PyTorch, etc. - Strong programming and debugging skills in system-level environments. - Deep understanding of software development methodologies and integration architectures. - Strong analytical, problem-solving, and performance optimization skills. Preferred Qualifications: - Experience designing AI-driven storage management systems. - Experience with vector search, semantic retrieval, and enterprise AI platforms. - Knowledge of storage protocols, file systems, object storage, and high-performance computing environments. - Experience in building enterprise-scale Agentic AI ecosystems and autonomous infrastructure platforms. - Track record of driving innovation, patents, or successful product deployments in Storage, Cloud, or AI domains. As an experienced AI Storage Architect at our company, you will be responsible for leading the design and development of next-generation AI-powered storage and cloud platforms. Your expertise in Storage Architecture, Data Path Engineering, Distributed Systems, Cloud Platforms, and AI/ML technologies will be crucial in building scalable, high-performance enterprise solutions. Key Responsibilities: - Design, develop, and deploy AI models, including LLMs, Agentic AI systems, NLP models, Computer Vision solutions, and Deep Learning applications. - Build autonomous storage management platforms powered by LLMs and Agentic AI for predictive capacity planning, anomaly detection, performance tuning, and self-healing operations. - Develop AI-powered tools and platforms using Python, R, TensorFlow, PyTorch, and modern AI frameworks. - Define storage-specific use cases for Generative AI and Agentic AI, including intelligent storage operations, automated troubleshooting, capacity optimization, and infrastructure governance. - Design and implement RAG (Retrieval-Augmented Generation) architectures integrating vector databases, storage systems, and enterprise knowledge repositories. - Architect and deploy multi-agent systems across Kubernetes, cloud services, edge environments, and enterprise application platforms. - Define agent lifecycle management, orchestration, memory management, communication protocols, governance, and security controls. - Design scalable architectures capable of supporting thousands of AI agents across multiple clouds and data centers. - Implement observability, monitoring, and governance frameworks for