Multimodal Data Infrastructure Expert

Join a technology organisation developing advanced data infrastructure for the emerging AI Agent ecosystem. Working at the intersection of data platforms, distributed computing and artificial intelligence, the company is creating systems designed to process and manage massive volumes of multimodal and unstructured information.

This senior-level role offers the opportunity to define and deliver a next-generation multimodal data platform for massive volumes of structured and unstructured data. The position combines distributed systems, heterogeneous compute, vector retrieval, intelligent storage, high-performance caching, and AI-driven database operations.

Role Overview

You will take ownership of the architecture and implementation of foundational services that schedule, process, query, store, and govern multimodal workloads at scale.

Your responsibilities will cover the full data lifecycle, including the allocation of resources across CPUs, GPUs, and NPUs, as well as the development of intelligent agents that automate engineering, analytics, operations, and system maintenance.

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.

Required Experience

• Advanced programming skills in languages such as C, C++, Python, or Java.
• Significant research or engineering experience in databases, distributed platforms, large-scale data systems, or high-performance computing.
• Strong knowledge of system design, architecture, and performance optimisation.
• Understanding of CPU, GPU, and NPU architectures, including the complexities of mixed-resource environments.
• Hands-on experience with resource scheduling, orchestration, or heterogeneous compute allocation.
• Background in building or operating cloud platforms.
• Familiarity with DevOps methodologies and tooling.
• Experience with query optimisers, execution frameworks, storage engines, distributed storage systems, or other core infrastructure components would be highly advantageous.
• Expertise in at least one the following areas: LLM Fine-tuning, Reinforcement Learning, NLP, Computer Vision, Multimodal Data Systems and/or AI for database, Data Platforms or infrastructure Automation.

This is an opportunity to tackle challenging infrastructure problems at the intersection of databases, distributed computing, specialised hardware, and agentic AI. You will play a significant role in shaping a platform intended to support the next generation of intelligent applications.

If you are an experienced systems engineer or researcher motivated by multimodal computing and autonomous data infrastructure, apply today or contact [email protected].

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