Data Infrastructure and AI Engineer

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. Explore cost models, adaptive execution, vectorised execution, parallel execution, and workload-aware optimisation.
• 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. Required skills: 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.

Requirements:

• 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.
• Proficiency in one or more deep learning programmatic interfaces, e.g., Python, TensorFlow.
• Ability to conduct empirical systems research, including workload analysis, benchmarking, profiling, experiment design, and performance interpretation.
• Strong problem-solving skills and the ability to work on open-ended research and engineering problems.
• Effective technical communication skills and a collaborative mindset.

If this role is of any interest please apply directly on LinkedIn or send a copy of your CV to nh@eu-recruit.com.

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