As a Manager of AI/ML Engineering team at JPMorgan Chase within the Business Banking , you lead our dynamic teams and will be responsible for applying advanced machine learning techniques to intricate task such as natural language processing, document screening and  manage day-to-day implementation activities by identifying and escalating issues 

Job responsibilities

• Develop state-of-the art machine learning models to solve real-world problems and apply it to tasks such as NLP, speech recognition and analytics, or recommendation systems
• Choosing, extending, and innovating ML strategies for various banking problems
• Provides guidance to immediate team of software engineers on daily tasks and activities
• Sets the overall guidance and expectations for team output, practices, and collaboration
• Anticipates dependencies with other teams to deliver products and applications in line with business requirements
• Collaborate with multiple partner teams such as Business, Technology, Product Management, Legal, Compliance, Strategy and Business Management to deploy solutions into production
• Learning about and understanding our supported businesses in order to drive practical and successful solutions

Required qualifications, capabilities, and skills

• Formal training or certification in Software Engineering concepts and 5+ years applied experience and hand-on industry experience in Machine Learning.
• Good understanding of the latest advancement of NLP concepts, such as the 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 the ability to invent and to work both 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

Preferred qualifications, capabilities, and skills

• 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