Senior Director, Machine Learning Engineering (Dublin / Hybrid)Location: Dublin, Ireland (Hybrid)
Contract Type: Permanent
Experience Level: Senior Leadership (10+ years)
Sector: FinTech / AI / Large Language Models
Salary: Competitive with benefits
About the CompanyOur client is a leading FinTech innovator harnessing large language models (LLMs) and machine learning to transform financial decision-making, compliance automation, and customer intelligence. Their platform empowers global financial institutions to analyse data more intelligently, streamline reporting, and reduce operational risk - all through cutting-edge applied AI.
The company has established itself as a trusted technology partner across Europe and North America, combining deep financial domain expertise with advanced engineering. Headquartered in Dublin, this is a fast-growing, well-funded business entering its next phase of expansion.
The OpportunityThe company is seeking a Senior Director of ML Engineering to lead and scale their established ML engineering organisation. This is a pivotal role reporting to the CTO, responsible for driving the end-to-end strategy, execution, and delivery of machine learning and LLM-powered systems across multiple product lines.
You will guide a talented group of engineers and applied scientists working on model training, deployment, and optimisation for large-scale financial data environments. This is an opportunity to shape how generative AI and predictive models power financial insight and automation in a highly regulated industry.
Key ResponsibilitiesStrategic Leadership: Define and execute the long-term ML and AI engineering strategy, aligning with company goals and product vision.
Team Development: Lead and mentor an established team of ML Engineers, Applied Scientists, and MLOps specialists, fostering technical excellence and continuous learning.
Technical Direction: Oversee the design, training, deployment, and monitoring of LLM and ML models in production at scale.
Cross-Functional Collaboration: Partner with Product, Data Science, and Platform Engineering to translate research into production-grade solutions.
