Principal Machine Learning Engineer (Fintech)

Location: Hybrid, with two days per week in the office

Salary: €92,000–€115,000 per year plus equity and benefits

Company

hirify.global provides AI-powered financial crime risk intelligence and automation for AML, KYC, sanctions, and fraud compliance.

What you will do

• Lead the architecture and implementation of company-wide MLOps and agentic AI platforms.
• Build training, evaluation, serving, feature-store, vector-store, and agent-orchestration capabilities.
• Translate ML and agentic AI roadmaps into scalable production systems aligned with data governance and compliance standards.
• Develop and productionize LLM, retrieval-augmented generation, multi-agent, and graph neural network systems.
• Set engineering standards for code quality, evaluation, observability, CI/CD, and operational reliability.
• Coach ML engineers, engage senior stakeholders, represent the company at industry forums, and support improvements to the hiring process.

Requirements

• Substantial experience building, training, and productionizing machine learning models at scale, including deep learning and large language models.
• Deep production Python experience and strong software engineering fundamentals, including event-driven architecture and observability.
• Strong mathematical and statistical foundations with the ability to apply techniques rigorously and defensibly.
• Experience designing MLOps platforms with training pipelines, feature and vector stores, serving infrastructure, and drift and performance monitoring.
• Experience with AWS, GCP, Kubernetes, Docker, ArgoCD, Argo Workflows, Kafka, batch processing, streaming, and ETL.
• Excellent written and verbal communication, technical documentation, stakeholder engagement, and experience coaching ML engineers.

Nice to have

• Experience applying ML, LLMs, and agentic AI in AML, KYC, fraud, RegTech, or another regulated domain.
• Knowledge of knowledge graphs, entity resolution, link analysis, and temporal reasoning.
• Experience creating evaluation frameworks and safety, accuracy, and operational guardrails for LLM and agentic systems.
• Conference speaking, publications, or open-source contributions in the ML community.

Culture & Benefits

• Hybrid work model with two office days per week.
• Equity participation and an unlimited time-off policy.
• Home-office equipment budget for new starters.
• Annual learning budget for professional development.
• Opportunities to work on innovative projects and share knowledge with experienced colleagues.