We are seeking an experienced AI Storage Architect to lead the design and development of next-generation AI-powered storage and cloud platforms. The ideal candidate will possess deep expertise in Storage Architecture, Data Path Engineering, Distributed Systems, Cloud Platforms, and AI/ML technologies, with a proven track record of building scalable, high-performance enterprise solutions.
This role requires hands-on experience in Agentic AI, LLM-based systems, autonomous infrastructure management, AI-enabled storage optimization, and cloud-native platform architectures.
Designation: AI StorageArchitect
Exp- 1 4+ yrs
Location- Bangalore (Hybrid)
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.
