Lead AI Applied ML Engineer

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 solutions 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. You will work at the intersection of Risk modeling, and 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.

Job responsibilities

• Lead a small group of ML engineers
• Lead and influence 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 experience in cloud based applications with 4+ years of experience as an MLE
• Strong foundation in Information Retrieval, Natural Language Processing and
• Expert in functional programming and JVM based languages- Python, Java
• Experience integrating models into cloud scale, microservices based architectures
• Experience with one or more ML frameworks - Pytorch, Tensorflow, SciKit, NeMo, Huggingface 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 env
• Operational experience in supporting an enterprise grade ML application in production

Preferred qualifications, capabilities, and skills

• Knowledge of Firm Databricks CDAO platform is good to have
• 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