Applied AI/ML Manager of Software Engineering
On-site · New York City, New York, United States
Job Summary
Lead and guide multiple AI/ML engineering teams at JPMorgan Chase within the Business Banking division, applying advanced machine learning techniques to tasks such as NLP, document screening, analytics, and recommendation systems. Define and extend ML strategies for banking problems, provide day-to-day technical leadership to a team of software engineers, set expectations for team output and practices, anticipate dependencies with other teams, and collaborate with Business, Technology, Product Management, Legal, Compliance, Strategy and Business Management to deploy solutions into production. Build practical, production-ready solutions by understanding supported businesses and driving data-driven, scalable outcomes.
Required Qualifications
- Formal training or certification in Software Engineering concepts and 5+ years applied experience and hands-on industry experience in Machine Learning
- Good understanding of NLP concepts, such as transformer architecture and knowledge distillation
- Experience in classical ML techniques including classification, clustering, optimization, cross validation, data wrangling, feature selection, and feature extraction
- Experience leading technology projects
- Experience managing technologists
- Proficient in automation and continuous delivery methods
- Scientific thinking with ability to invent and work independently and in highly collaborative team environments
- Solid written and spoken communication skills
- Proficient in all aspects of the Software Development Life Cycle
- Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
- Experience in Computer Science, Engineering, Mathematics, or a related field and expertise in technology disciplines
- Hands-on experience with virtual assistant model development and optimization
- Familiarity with continuous integration models and unit test development
- Experience with A/B experimentation and data/metric-driven product development
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