Top AI & Machine Learning Jobs

Lead the AML Risk data science team and develop machine learning, GenAI, and statistical solutions for global anti-money laundering detection. Build supervised and unsupervised detection controls, evaluate and optimize model performance, create investigator insights, improve operational processes, and deploy production-grade Python services and automated retraining pipelines. Responsibilities also include hiring, mentoring, cross-functional collaboration, and managing projects that improve model training and financial crime detection.

Build and maintain scalable machine learning platform services, including model serving, training pipelines, experiment tracking, monitoring, and developer tooling. Improve reliability, observability, cost efficiency, and user experience for internal data scientists and ML engineers. Contribute to architecture, roadmap planning, on-call support, code reviews, documentation, mentoring, and continuous improvement.

Design and architect large-scale conversational AI systems and agents for customer support. Own prompts, context and knowledge architecture, define agent behaviour, and collaborate with engineers, product and data science to deliver automation strategy, coach teams, and raise content and model-design maturity.

Own and operate production Python infrastructure supporting FX pricing, risk analytics, curve construction, and trading systems. Responsibilities include developing microservices and shared quantitative libraries, managing monitoring and alerting, maintaining CI/CD pipelines, responding to incidents, and improving system reliability and performance. The role also offers growth into pricing models, trading strategies, risk modeling, backtesting, and customer behavior modeling while collaborating with quants, traders, analysts, product managers, and engineers.

Lead development of the label integrity layer for Risk ML: ensure label quality, define statistical metrics, build automated audits, monitor label drift, and work end-to-end on model training, evaluation, and deployment in collaboration with Risk Intelligence, Data Engineering, and Product teams.

Build and maintain the integrity layer for label data used in FinCrime ML models. Define and monitor statistical quality metrics, design automated label audit processes, and work end-to-end on model training, evaluation, and deployment in collaboration with Risk Intelligence, Data Engineering, and Product teams.

Lead development of an AI-powered assistant and route-by-route activation strategy to convert first-time cross-border senders. Build scalable frameworks, run data-driven experimentation (A/B tests, funnels, cohorts), partner with engineering, compliance, pricing and regional teams, and guide designers and analysts to deliver localized, conversational transfer experiences that increase conversions and reduce customer effort.

Lead development and deployment of ML systems (neural nets, graph models, Transformers) for AML detection. Mentor team members, integrate models with production platforms and LLM agents, design data strategies, run large-scale training and evaluation, and communicate results to stakeholders.

Lead design, build, and deploy scalable microservices and AI-driven contact automation for the Wise Assistant. Own intent detection, contact processing, routing logic, LLM/RAG integrations (AWS Bedrock), third-party AI agent integrations, performance and reliability optimizations, and mentor engineers while collaborating with cross-functional teams to automate customer support issues.

Lead and grow the Generative AI Platform function, guiding engineering leads to build tools, guardrails and production systems for LLM-based applications. Ensure reliability, security, observability and operational excellence across cloud microservices while collaborating with product teams, negotiating vendors, mentoring engineers, and shaping AI enablement and controls across the company.