Global Financial Crimes Tech - Applied AI ML Director
Salary not listed • LONDON, United Kingdom • Full-time
Posted 1d ago
Job Location
LONDON, United Kingdom
Tech Stack
Remote Work Policy
On-site
Employment Type
Full-time
Categories
LLM Engineer
About the job
We are seeking an AI ML Director to lead a team in building next-generation, AI-driven Global Financial Crimes Strategic Monitoring solutions. This role involves detecting AML risk, regulatory violations, transaction risk, misconduct, and behavioral anomalies. You will be responsible for designing, building, and productionizing risk typologies, features, data pipelines, supervised and unsupervised ML models, and LLM risk explainability at scale. This is a hands-on role requiring deep expertise in ML operations, LLM integration, scalable ML systems, and production-grade engineering discipline, with opportunities to collaborate with product managers, architects, data science, and operational teams.
Responsibilities
- Lead a team of ML engineers to deliver AI solutions in production.
- Design, build, and operate ML models and LLM solutions.
- Design and build feedback and accuracy measurement techniques for AI solutions.
- Design, build, and operate risk features data pipelines in Databricks.
- Monitor for drift, bias, and performance degradation in AI solutions.
- Work within a cross-functional team using agile processes.
- Collaborate with Product Managers, SRE, and Compliance SMEs to improve product adoption, reliability, and outcomes.
Requirements
- Formal training or certification in Computer Science, Data Science, or AI concepts with advanced applied experience.
- PhD, MS, or BS in Computer Science, Data Science, AI, or similar fields.
- Deep foundation in Information Retrieval and Natural Language Processing.
- Expertise in functional programming and JVM-based languages like Python and Java.
- Experience integrating models into cloud-scale, microservices-based architectures.
- Hands-on experience with ML frameworks such as PyTorch, TensorFlow, SciKit, NeMo, or Huggingface Transformers.
- Hands-on experience with AWS services and Databricks.
- Experience with SQL, NoSQL, and messaging stacks.
- Excellent verbal and written communication skills with a bias for action and ownership.
- Operational experience supporting an enterprise-grade ML application in production.