How a Dublin Fintech Scaled It's AI Engineering Capability in 2026
Client Overview
A rapidly growing Dublin‑based fintech specialising in fraud detection and real‑time payments set out to scale its AI capability in 2026. As the company transitioned from traditional data‑science workflows to production‑level machine‑learning systems, new challenges emerged around staffing, governance, and operationalising AI at scale.
Challenge
The organisation faced escalating demand for:
Hybrid engineering roles blending classical software engineering with applied AI skills
AI validation, auditability and compliance, driven by sector regulation and the EU AI Act
MLOps and automation capability to support model deployment, monitoring and continuous improvement
The existing team structure could not support the organisation’s expanding AI roadmap, which introduced risks across development speed, reliability and regulatory oversight.
Solution
Experis partnered with the organisation to assess capability gaps and help define a talent model suitable for a scalable AI function. Three core priorities emerged.
1. Building Hybrid AI + Software Engineering Capability
The organisation identified a critical skills gap:
Software engineers lacked the AI literacy to integrate LLM‑supported models
Data scientists lacked the engineering depth to productionise reliably
To solve this, they introduced an AI Platform Engineer role blending: Python engineering, cloud‑native architecture, LLM integration, vector databases, event‑driven systems and AI evaluation tooling.
Impact: Deployment timelines dropped from six weeks to twelve days, with engineering and data teams finally operating on a shared, production‑ready workflow.
2. Establishing AI Validation & Testing Frameworks
In a regulated financial environment, the company needed stronger AI governance. A dedicated AI Validation Function was created to manage:
Robustness & scenario testing
Drift detection
Adversarial assessments
Automated quality checks
Audit‑aligned documentation
Impact: Model reliability improved significantly and false positives in fraud detection fell by 18% within three months.
3. Scaling MLOps & Automation
Rising experimentation volume made deployment and orchestration increasingly complex. The organisation invested in MLOps Automation Engineers skilled in CI/CD for ML, containerised runtimes, feature/model versioning, cross‑environment orchestration and observability.
Impact: Operational overhead reduced by 40%, and deployments that once took days were completed in hours.
Outcomes
Within one quarter, the organisation achieved the following results:
AI engineering function scaled from 3 to 11 specialists
First production-ready LLM‑supported fraud‑scoring pipeline deployed
Zero findings in an external regulatory review
Model deployment timelines reduced by 65%
AI roadmap expanded from 2 to 9 initiatives
Faster iteration cycles supported innovation across fraud, risk and payments
