AI Data Platform Lead

Manchester UK
, hybrid (60% office presence per year after onboarding period)

Red Compass Labs are payments experts focused exclusively on payments modernization. Over the last 23 years, Red Compass Labs has delivered 500+ projects across the world’s major payments schemes and platforms, including ISO 20022, SEPA, Swift, the Real-time Payments Network, and Fed Now, ISO 20022 migrations, and FIS OPF, Finastra GPP, and ACI Worldwide.

Its flagship Payments Expert Agent is an AI solution purpose-built for the full payments modernization software development lifecycle. Trained on decades of deep payments expertise and more than 30,000 curated documents, the agent is certified to SOC 2 Type II.

Red Compass Labs supports clients from offices in London, Warsaw, Brussels, Miami, Boston, Toronto, and Pune. For more information, visit

Imagine reducing a 14-day financial analysis task to just 2 hours. At Red Compass Labs, we don't just imagine-we've built it.

We're transforming the trillion-dollar payments industry through intelligent automation. At the heart of our mission is Analyst Accelerator.ai, our proprietary multi-agent AI platform built to accelerate complex analysis in global banking and compliance.

We’re seeking AI Data Platform Lead to own the data foundation the Payments Expert Agent depends on. Reporting to our Head of AI Engineering, you'll own the ETL platform behind our internal payments knowledgebase and the benchmarking strategy that tells us whether our agents and knowledge are actually any good. You'll line manage our Dev Ops team, working in a regulated, high-stakes environment where the quality of what the agent knows is the product.

You will own the data platform and evaluation capability that determine how good our AI products actually are:

• Own the ETL data platform that services and maintains our internal payments knowledgebase - ingestion, transformation, refresh, and quality.
• Ensure a steady flow of accurate, real-time data into our knowledge stores.
• Own AI agent and knowledge benchmarking - defining the strategy and building the implementation that measures agent and retrieval quality.
• Develop infrastructure strategy and implementation for AI product deployment.
• Develop observability of AI agent and knowledge usage - strategy and implementation.
• Own the strategy to product ionise internally trained LLMs - taking models from research into deployable form.
• Lead
  our SRE and Observability team

We’re seeking a hands-on technical leader who can set direction and build the thing themselves when it matters.

• Track record building and owning production data platforms - ETL/ELT at scale, not just pipeline contributions.
• Financial services experience, or experience with other highly regulated data sources.
• Experience with RAG and knowledge retrieval systems, including how retrieval quality is measured and improved.
• Hands-on with LLM and agent evaluation - benchmarking, eval harnesses, and regression testing of non-deterministic systems.
• Python experience, and comfortable in an Azure-native environment.
• Experience setting technical strategy and seeing it through to implementation - across data, infrastructure, and evaluation.
• Experience line managing a technical team, including Dev Ops or platform engineers, with the judgement to know when to delegate and when to go hands-on.
• Comfortable operating where the data is the product - you understand that knowledge quality determines whether the agent is trusted.

• Background in financial services, payments, or another regulated domain.
• Experience product ionising trained or fine-tuned models, including serving and monitoring.
• Experience with data platforms…