Lead AI Applied ML Engineer
About The Position
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.
Requirements
- 8+ years experience in cloud based applications with 4+ years of experience as an MLE
- Strong foundation in Information Retrieval, Natural Language Processing
- 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
Nice To Haves
- 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
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
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
