Principal AI Engineer [Risk]

Join Plata as a Principal AI Engineer to lead the foundation model program, driving credit decisions with innovative AI solutions in a fast-growing fintech environment.

Plata is one of the fastest-growing fintech companies in the world. In just 3 years, we've grown to 3M+ customers and reached a $5B+ valuation. We're now strengthening our Risk & Decisioning core team and are looking for a Principal AI Engineer to set the technical bar and build best-in-class, production-grade models that materially move business metrics.

• •Build an end-to-end foundation model over financial event sequences - transactions, credit bureau data, and in-app behavioral events with subsequent fine-tuning for downstream business tasks: underwriting (PD), credit limit strategy, fraud detection, collections, and propensity models.
• •Drive technical decisions end-to-end: methodology → implementation → performance and latency → robustness, interpretability, and regulatory compliance.
• •Take models from research to production: training infrastructure, evaluation frameworks, model serving, latency/cost optimization, and monitoring.
• •Research state-of-the-art approaches in the industry, publish your own work, and speak at leading conferences.
• •Mentor senior engineers and scientists; own technical standards for model development across the team (design reviews, evaluation methodology, deployment practices).
• •Communicate results clearly to cross-functional stakeholders: product, risk, business, and leadership.

• •Relocation support to one of our hubs - Mexico, Cyprus, Serbia, Spain with assistance for the employee and their family.
• •Flexible work from one of our offices or remote.
• •Healthcare coverage.
• •Education budget: language lessons, professional training and certifications.
• •Wellness budget: mental health and fitness activity reimbursements.
• •Vacation policy: 20 days of annual leave and paid sick leave.

• •Proven experience applying deep learning to sequential data - transformer architectures on event/transaction sequences strongly preferred, but not required.
• •Strong foundation in mathematical statistics and probability theory.
• •Deep understanding of machine learning algorithms (GBM, MLP, CNN, RNN, Transformers, etc.)
• •Experience taking large models to production: distributed training, model serving, latency/cost trade-offs.
• •Ability to strike a reasonable balance between solution complexity and practical applicability.
• •Strong mathematical or technical education - degree in mathematics, physics, or CS from a top technical university.
• •Kaggle Competitions Master/Grandmaster or equivalent (a plus); experience developing models in banking or consumer lending (a plus).
• •Strong communication skills.