Data Platform Engineer for AI Training Environments

Dex; Greater London; Hybrid; GBP 90,000 - 130,000; Full time

Job summary

Dex is partnering with a seed-stage research lab in London to recruit a data platform engineer who will own end-to-end data infrastructure for AI training environments. You will design scalable ingestion, processing, and storage systems, and implement automated validation and sanitisation pipelines for large real-world datasets.

You will work across software, platform, and AI engineering boundaries to optimize latency, observability, and cost while staying close to production code.

Qualifications

• Proven track record designing, shipping, and operating large-scale data systems end to end.
• Strong software fundamentals and ability to debug unfamiliar codebases.
• Experience handling messy datasets with ingestion, validation, or sanitisation bottlenecks.
• Ability to define system architecture from ambiguous requirements and deliver production systems.

Responsibilities

• Architect and operate scalable ingestion, processing, and storage systems for high-volume datasets.
• Build automated validation, sanitisation, and PII-redaction pipelines for AI training data.
• Balance storage, latency, observability, reliability, and cloud costs through pragmatic trade-offs.
• Establish reusable data platform patterns for other engineers to reuse.
• Debug production failures across the stack and deploy modern tooling to keep feedback loops tight.

Skills

Distributed systems

Data ingestion

PII sanitisation

Observability

Cloud costs

Tools

Airflow

Spark

SQL

Job description

Dex is partnering with a seed-stage research lab in London to recruit a data platform engineer who will own end-to-end data infrastructure for AI training environments. You will design scalable ingestion, processing, and storage systems, and implement automated validation and sanitisation pipelines for large real-world datasets.

You will work across software, platform, and AI engineering boundaries to optimize latency, observability, and cost while staying close to production code.