Machine Learning Engineer

The Role

Lead our ML efforts building domain-specific AI models for payment processing. You'll design, train, and deploy models for fraud detection, risk assessment, transaction categorization, and agentic authorization systems.

This role is perfect if you want to:

• Apply cutting-edge ML (transformers, LLMs) to real-world, high-stakes problems
• See your models protect billions in transactions
• Work at the intersection of research and production
• Build a team of ML engineers over time

What You'll Do

Model Development (50%)

• Design and implement fraud detection models (gradient boosting, neural nets, transformers)
• Fine-tune LLMs for payment domain (transaction understanding, risk reasoning)
• Build agentic AI systems for real-time authorization decisions
• Develop feature engineering pipelines for payment data
• Create embeddings and vector representations for merchants, transactions, cardholders

Production ML (30%)

• Deploy models to production (low-latency inference, high throughput)
• Build MLOps pipelines: training, evaluation, monitoring, retraining
• Optimize model performance (latency <100ms, 10K+ TPS)
• Implement A/B testing framework for model experiments
• Monitor model drift and performance degradation

Research & Innovation (20%)

• Stay current on latest ML research (LLMs, adversarial ML, federated learning)
• Prototype new approaches (RAG for fraud investigation, graph neural nets for transaction networks)
• Publish research findings (blog posts, papers, conference talks)
• Collaborate with academic partners on AI security research

What You Bring

Required:

• 3+ years ML experience, 1+ years deploying production ML systems
• Deep expertise in modern ML: transformers, LLMs, deep learning frameworks (PyTorch/TensorFlow)
• Strong software engineering skills (Python, production-quality code)
• Experience with ML infrastructure: training pipelines, serving systems, monitoring
• Track record of shipping models that drive business impact
• Strong fundamentals: statistics, optimization, evaluation metrics

Strongly Preferred:

• Experience fine-tuning or deploying LLMs (Llama, Mistral, GPT)
• Background in fraud detection, risk modeling, or financial ML
• Knowledge of adversarial ML and model security
• Experience with feature stores, real-time inference systems
• Familiarity with payment data (transactions, merchants, cards)
• Published research or Kaggle medals

Nice to Have:

• MS/PhD in ML, Computer Science, or related field
• Experience with specialized ML: federated learning, privacy-preserving ML, continual learning
• Domain expertise in NLP (transaction text understanding)
• Experience building or using agentic AI systems
• Track record mentoring ML engineers

Job Type: Full-time

Pay: €40,918.37-€97,104.91 per year

Benefits:

• Company pension
• Work from home

Application question(s):

• Do you currently have the legal right to work in Ireland for this role without requiring employer sponsorship now or in the future?

Education:

• Master's (preferred)

Experience:

• Machine learning: 3 years (required)
• Python: 3 years (required)
• Software deployment: 2 years (required)

Work authorisation:

• Ireland (required)

Work Location: In person