This is your chance to change the path of your career and guide multiple teams to success at one of the world's leading financial institutions.

As a AI/ML Engineering lead at JPMorgan Chase within the Business Banking , you will be responsible for applying advanced machine learning techniques to intricate task such as natural language processing, document screening and define the pattern to be adopted by the team.

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
• 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