Data Infrastructure and AI Engineer European Tech Recruit are working closely with a leading telecommunications company, based in Edinburgh, who are looking for a talented Data Infrastructure and AI Engineer to join their world-leading research center. In this role you will join a database team that develops next-generation data management systems, with a focus on database kernels, query processing, storage engines, transaction processing, distributed data systems, and emerging AI Data infrastructure. This role will be a perfect fit for engineers with experience in one or more of the following areas: database systems, on-device AI, query optimization, query execution engines, storage and indexing, transaction processing, concurrency control, distributed databases, cloud-native data systems, hardware-aware database design, AI-native data management, and performance analysis. Responsibilities as Data Infrastructure and AI Engineer: Design, implement, and evaluate core components of next-generation database and data management systems, including query optimisers, execution engines, storage engines, indexing structures, transaction processing, and distributed data processing frameworks. Research and prototype advanced query planning and execution techniques for transactional, analytical, hybrid, and AI-driven workloads. Develop efficient storage and indexing mechanisms for structured, semi-structured, multimodal, and AI-oriented data. Investigate data layout, caching, compression, memory hierarchy optimisation, and hardware-aware storage engine design. Explore distributed database architectures, data partitioning, replication, fault tolerance, distributed query execution, resource scheduling, and cloud-native data management techniques for large-scale deployment environments. Investigate database support for emerging AI workloads, including vector search, retrieval-augmented generation, agent memory, semantic data management, knowledge graph integration, multimodal data management, and AI-assisted query/data processing. Develop techniques that can run on-device and can power the next generation of AI applications. LLM quantization, on-device LLM inference, supervised and unsupervised LLM fine-tuning, parameter-efficient fine-tuning, knowledge distillation, gradient-free learning, memory for agentic AI. Master's, or PhD degree in Computer Science, Computer Engineering, Electrical Engineering, Mathematics, or a related discipline. Strong background in computer systems, database systems, AI systems, distributed systems, operating systems, or related areas. Solid understanding of database system principles, such as query processing, query optimization, storage engines, indexing, transaction processing, concurrency control, recovery, or distributed data management. Solid understanding of AI system principles, such as LLM quantization, on-device LLM inference, supervised and unsupervised LLM fine-tuning, parameter-efficient fine-tuning, knowledge distillation, gradient-free learning, memory for agentic AI. Hands-on experience in system design, implementation, evaluation, and performance debugging. Proficiency in one or more system-level programming languages, such as C, C++, Rust, or Go. Python, TensorFlow. Ability to conduct empirical systems research, including workload analysis, benchmarking, profiling, experiment design, and performance interpretation. By applying to this role you understand that we may collect your personal data and store and process it on our systems.
