Backend Data Platform & AI Engineer | Investment Firm

Our client, a leading boutique investment firm, is looking for a Backend Data Platform & AI Engineer in London to own the operational data and AI layer supporting quantitative research and analytics, allowing downstream teams to focus on modelling.

The role acts as the internal bridge across backend engineering, data platforms and AI workflows, working directly with quantitative and analytics teams while also acting as a technical interface for external data vendors and DevOps consultants. The position exists to take hands-on ownership of ingestion pipelines, automated data cleaning, batch processing, quantitative storage and internal AI/LLM infrastructure. For an early-career engineer, it offers unusually broad technical ownership across the infrastructure underpinning research and analytics.

Responsibilities

• Build, deploy and maintain automated data-ingestion connectors and data-cleaning processes, including deduplication, normalisation and schema enforcement.
• Manage and optimise storage architectures supporting time-series and cross-sectional datasets.
• Support and maintain internal AI/LLM workflows, model endpoints and data-preprocessing pipelines using cloud AI infrastructure.
• Implement automated logging, data validation and failure alerting across scheduled ingestion and data-preparation jobs.
• Maintain containerised workers and security controls while leading technical discussions with external data vendors and DevOps consultants.

Requirements

• 1-10 years' experience, with a strong project track record and evidence of independently solving technical problems.
• Quantitative academic background across Physics, Mathematics, Computer Science, Engineering, Quantitative Finance or Economics.
• Backend engineering capability in Python, Go or Java, with exposure to API-driven data ingestion.
• Relevant experience across data storage, batch processing, validation and cloud infrastructure.
• Evidence of a self-taught trajectory, independent problem-solving and the ability to take meaningful technical ownership early in your career.

Why Apply?

This is particularly compelling for an early-career engineer who wants broader ownership than a narrowly defined backend or data role. You will sit across data engineering, backend infrastructure and emerging AI workflows, with your work directly enabling quantitative and analytics teams to concentrate on modelling. If you already have a strong quantitative foundation and have demonstrated that you can build independently, this offers the chance to take responsibility across a genuinely broad technical surface area early in your career.

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