Multimodal Data Infrastructure Expert
European Tech Recruit
Multimodal Data Infrastructure Expert
Join a technology organisation developing advanced data infrastructure for the emerging AI Agent ecosystem. This senior-level role involves defining and delivering a next-generation multimodal data platform for massive volumes of structured and unstructured data, combining distributed systems, heterogeneous compute, vector retrieval, intelligent storage, high-performance caching, and AI-driven database operations.
Multimodal Data Infrastructure Expert
Join a technology organisation developing advanced data infrastructure for the emerging AI Agent ecosystem. This senior-level role involves defining and delivering a next-generation multimodal data platform for massive volumes of structured and unstructured data, combining distributed systems, heterogeneous compute, vector retrieval, intelligent storage, high-performance caching, and AI-driven database operations.
Salary
Core Qualifications
Technical (Must-have)
Preferred Qualifications
Technical (Nice-to-have)
Key Responsibilities
- Build an abstraction layer for coordinating CPU, GPU, and NPU resources.
- Design serverless mechanisms for sharing and assigning compute across multiple execution engines.
- Enhance scheduling, throughput, elasticity, and overall hardware efficiency.
- Develop infrastructure for processing text, images, video, time-series, geospatial, vector, and other complex datasets.
- Create sophisticated optimisation techniques for multimodal queries.
- Implement hybrid execution models that support efficient search and analysis across varied data types.
- Establish a consolidated system for vector indexing, search, persistence, metadata, permissions, and access management.
- Integrate suitable open-source components and technologies.
- Improve data placement, indexing methods, lifecycle management, and automated index maintenance.
- Create high-speed caching services that keep frequently used multimodal data near processing resources.
- Support rapid data exchange between distributed compute engines.
- Reduce pipeline latency and eliminate performance constraints in demanding workloads.
- Use database-focused AI methods and LLM agents to make data platforms more autonomous.
- Develop agents and reusable capabilities for workload creation, storage management, analysis, operations, and maintenance.
- Convert advances in Data and AI research into robust, production-ready systems.