Data Infrastructure and AI Engineer - Database Systems / AI Infrastructure / Distributed System[...]

European Tech Recruit; City of Edinburgh; On-site; GBP 90,000 - 130,000; Full time

Job summary

European Tech Recruit is seeking a Data Infrastructure and AI Engineer to work at the intersection of database systems, distributed infrastructure, machine learning systems, and low-level computing. The role focuses on designing, evaluating, and advancing data infrastructure and AI systems for next-generation workloads.

The ideal candidate has a strong background in database/IR architecture, low-level system design, and experience with AI workloads and RAG systems.

Qualifications

• Master’s or PhD degree in Computer Science, Computer Engineering, Electrical Engineering, Mathematics, or related technical discipline.
• Contributions to database systems, data processing engines, storage systems, distributed systems, compilers, operating systems, or other low-level infrastructure projects.
• Experience with hardware-conscious system design and optimisation.
• Familiarity with multi-core CPUs, NUMA, RDMA, CXL, NVM, SSDs, GPUs, NPUs, or heterogeneous computing architectures.
• Knowledge of vector search and embedding management.
• Experience with Retrieval-Augmented Generation (RAG) systems.
• Knowledge of knowledge graphs and semantic data management.
• Experience developing memory systems or infrastructure for agentic AI.
• Experience optimising systems for AI workloads and large-scale data processing.
• Research publications in leading database, systems, or AI infrastructure conferences and journals.
• Experience translating academic research into production-quality systems or prototypes.

Responsibilities

• Design, implement, and evaluate next-generation data infrastructure and AI systems.
• Research and develop innovative approaches to database systems, distributed data management, and AI infrastructure.
• Investigate database architecture, query processing, query optimisation, storage engines, indexing, transaction processing, concurrency control, recovery, and distributed data management.
• Develop and optimise systems supporting modern AI workloads, including large language models and agentic AI applications.
• Research techniques including LLM quantisation, on-device inference, supervised and unsupervised fine-tuning, parameter-efficient fine-tuning, knowledge distillation, and gradient-free learning.
• Investigate memory architectures and data management techniques for agentic AI systems.
• Develop system prototypes and conduct rigorous empirical evaluations.
• Analyse workloads and identify system-level performance bottlenecks.
• Design and execute benchmarks, experiments, and performance evaluations.
• Profile complex systems and diagnose performance, scalability, and efficiency issues.

Skills

Database systems

Distributed systems

Low-level infra

C/C++/Rust

Python

RAG systems

Knowledge graphs

Vector search

Education

Master’s or PhD in CS/CE/Math

Job description

Data Infrastructure and AI Engineer - Database Systems / AI Infrastructure / Distributed Systems / Systems Research

We are currently partnered with an advanced technology and research organisation developing next-generation data infrastructure, AI systems, and computing technologies.

As part of their continued investment in advanced systems research, they are looking to hire a Data Infrastructure and AI Engineer to work at the intersection of database systems, distributed infrastructure, machine learning systems, and low-level computing.

Key responsibilities

• Design, implement, and evaluate next-generation data infrastructure and AI systems
• Research and develop innovative approaches to database systems, distributed data management, and AI infrastructure
• Investigate database architecture, query processing, query optimisation, storage engines, indexing, transaction processing, concurrency control, recovery, and distributed data management
• Develop and optimise systems supporting modern AI workloads, including large language models and agentic AI applications
• Research techniques including LLM quantisation, on-device inference, supervised and unsupervised fine-tuning, parameter-efficient fine-tuning, knowledge distillation, and gradient-free learning
• Investigate memory architectures and data management techniques for agentic AI systems
• Develop system prototypes and conduct rigorous empirical evaluations
• Analyse workloads and identify system-level performance bottlenecks
• Design and execute benchmarks, experiments, and performance evaluations
• Profile complex systems and diagnose performance, scalability, and efficiency issues

Key requirements

• Master's or PhD degree in Computer Science, Computer Engineering, Electrical Engineering, Mathematics, or a related technical discipline
• Contributions to database systems, data processing engines, storage systems, distributed systems, compilers, operating systems, or other low-level infrastructure projects
• Experience with hardware-conscious system design and optimisation
• Familiarity with multi-core CPUs, NUMA, RDMA, CXL, NVM, SSDs, GPUs, NPUs, or heterogeneous computing architectures
• Knowledge of vector search and embedding management
• Experience with Retrieval-Augmented Generation (RAG) systems
• Knowledge of knowledge graphs and semantic data management
• Experience developing memory systems or infrastructure for agentic AI
• Experience optimising systems for AI workloads and large-scale data processing
• Research publications in leading database, systems, or AI infrastructure conferences and journals
• Experience translating academic research into production-quality systems or prototypes

Keywords

Data Infrastructure / AI Infrastructure / Data Infrastructure Engineer / AI Engineer / Systems Engineer / Research Engineer / Database Engineer / Database Systems / AI Systems / Distributed Systems / Computer Systems / Operating Systems / Database Internals / Query Processing / Query Optimisation / Storage Engines / Indexing / Transactions / Concurrency Control / Distributed Data Management / Cloud-Native Databases / HTAP / Vector Databases / Graph Databases / Lakehouse / AI-Native Data Platforms / PostgreSQL / MySQL / DuckDB / Spark / Flink / Velox / ClickHouse / RocksDB / TiDB / CockroachDB / LLM / Large Language Models / LLM Quantisation / LLM Inference / On-Device AI / Fine-Tuning / PEFT / Knowledge Distillation / Agentic AI / AI Memory / Vector Search / Embeddings / RAG / Retrieval-Augmented Generation / Knowledge Graphs / Semantic Data / C / C++ / Rust / Go / Python / TensorFlow / GPU / NPU / NUMA / RDMA / CXL / NVM / SSD / Heterogeneous Computing / Performance Optimisation / Benchmarking / Profiling / Systems Research / AI Infrastructure / Data Systems / Distributed Computing

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