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 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