Matrix Global Services is seeking a Technology Lead specializing in ML/AI and Generative AI to enhance their Anti-Money Laundering (AML) and Financial Crime initiatives within the Risk & Compliance function. The goal is to improve detection, reduce false positives, identify data anomalies, and provide actionable insights through advanced analytics and dashboards.
Requirements
- 7+ years of experience in ML/AI solution design and implementation, preferably within financial services or AML / financial crime domain.
- Strong experience with Python, SQL, and ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch).
- Experience in data analytics, feature engineering, and anomaly detection on large structured and unstructured datasets.
- Hands-on expertise in dashboarding and visualization tools (e.g., Power BI, Tableau, or similar).
- Knowledge or exposure to Generative AI (e.g., LLMs, prompt engineering, text summarization, or document intelligence) for compliance use cases.
- Experience with cloud platforms (AWS, Azure, or GCP) and MLOps pipelines.
- Prior exposure to Actimize, OFSAA, Quantexa, SAS AML, or similar financial crime platforms.
Responsibilities
- Define and drive the ML/AI and GenAI strategy and roadmap for AML and Financial Crime functions.
- Collaborate with compliance, risk, data, and technology teams to identify high-impact use cases for ML/AI (e.g., alert optimization, entity resolution, anomaly detection, segmentation, and false positive reduction).
- Lead design and implementation of data validation, quality checks, and lookback analyses as required by MRA (Matters Requiring Attention) or regulatory reviews.
- Develop and maintain dashboards and analytical insights for senior management and model governance reporting.
- Apply ML and GenAI techniques to enhance AML workflows—such as transaction monitoring, name screening, KYC/CDD reviews, and case investigation efficiency.
- Implement governance and safety frameworks for all ML/AI solutions including human-in-the-loop integration, bias mitigation, and LLM safety guardrails for audit and regulatory review.
- Work closely with data engineering teams to ensure data pipelines, features, and model outputs are production-grade and auditable.
Other
- Solid understanding of banking risk, AML/KYC processes, regulatory obligations, and MRA-driven remediation work.
- Familiarity with model validation, governance, and regulatory documentation.
- Excellent communication and stakeholder management skills — ability to translate technical insights into business language.
- Experience working with regulatory and audit teams in validating model and data integrity.
- Mentor and guide analysts and data scientists, ensuring delivery excellence and compliance with data governance policies.
