Senior Applied AI ML Engineer

About the role

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level. 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 Senior 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 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. This role offers a chance to collaborate with product managers, architects, data science and operational teams, while also engaging in software engineering communities to explore new and emerging technologies.

Responsibilities

• 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

Requirements

• PhD in Computer Science, Data Science, AI or similar fields with 2+ years of experience or
• MS in Computer Science, Data Science, AI or similar fields with 4+ years of experience or
• BS in Computer Science, Data Science, AI or similar fields with 8+ years of experience
• 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 & written communication skills and bias for action and ownership in early-stage environments
• Operational experience supporting an enterprise-grade ML application in production

Preferred Qualifications

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