Lead AI Applied ML Engineer

About the role

We are building a next generation, AI-driven Global Financial Crimes Strategic Monitoring solution that detects AML risk, regulatory violations, transactions risk, misconduct, and behavioral anomalies. As a Lead MLE on the team, you will design, build and productionize risk typologies/features, data pipelines, supervised and unsupervised ML models, and LLM risk explainability that operate at scale across high-volume banking transactions. You will be responsible for leading a team of 4 to 5 ML engineers and work at the intersection of risk modeling, NLP architectures, inference systems, regulatory explainability, and auditability. This is a hands-on senior role requiring deep expertise in ML operations, LLM integration, scalable ML systems, and production-grade engineering discipline.

Responsibilities

• Lead a small group of ML engineers
• Lead and influence the team with development and operational standards adherence
• Design, build, collaborate, and operate ML models
• Design, build, and operate LLM solutions
• Design and build feedback and accuracy measurement techniques for AI solutions
• Design, build, and operate risk features data pipelines in Databricks
• Conduct monitoring to detect and alert drift, bias, and performance degradation
• Work closely within a cross-functional team following agile-based processes
• Collaborate closely with Product Managers, SRE, and Compliance SMEs to continuously improve product adoption, reliability, and outcomes

Required Qualifications, Capabilities, and Skills

• 8+ years of experience in cloud-based applications with 4+ years of experience as an MLE
• Strong foundation in Information Retrieval and Natural Language Processing
• Expertise in functional programming and JVM-based languages—Python, Java
• Experience integrating models into cloud-scale, microservices-based architectures
• Hands-on experience with one or more ML frameworks—PyTorch, TensorFlow, SciKit, NeMo, Hugging Face Transformers
• Hands-on experience with AWS services and Databricks
• Experience/Exposure to SQL, NoSQL, and messaging stacks
• Excellent verbal and written communication skills and a bias for action and ownership in an early-stage environment
• Operational experience in supporting an enterprise-grade ML application in production

Preferred Qualifications, Capabilities, and Skills

• Knowledge of the Firm’s Databricks CDAO platform is a plus
• Experience with building production-grade ML pipelines, APIs, and MLOps frameworks
• Experience in AML monitoring and investigations systems is a strong plus
• Good understanding of data engineering concepts, distributed systems, and scalable architectures
• Familiarity with vector databases, model serving, and inference optimization is a plus