Applied AI/ML Lead

As a Data Scientist Lead at JPMorgan Chase within the Asset and Wealth Management, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm s business objectives.

Job responsibilities

• Design, deploy and manage prompt-based models on LLMs for various NLP tasks in the financial services domain
• Conduct research on prompt engineering techniques to improve the performance of prompt-based models within the financial services field, exploring and utilizing LLM orchestration and agentic AI libraries.
• Collaborate with cross-functional teams to identify requirements and develop solutions to meet business needs within the organization
• Communicate effectively with both technical and non-technical stakeholders
• Build and maintain data pipelines and data processing workflows for prompt engineering on LLMs utilizing cloud services for scalability and efficiency.
• Develop and maintain tools and framework for prompt-based model training, evaluation and optimization
• Analyze and interpret data to evaluate model performance to identify areas of improvement

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience
• Experience with prompt design and implementation or chatbot application
• Strong programming skills in Python with experience in PyTorch or TensorFlow
• Experience building data pipelines for both structured and unstructured data processing.
• Experience in developing APIs and integrating NLP or LLM models into software applications
• Hands-on experience with cloud platforms (AWS or Azure) for AI/ML deployment and data processing.
• Excellent problem-solving and the ability to communicate ideas and results to stakeholders and leadership in a clear and concise manner
• Basic knowledge of deployment processes, including experience with GIT and version control systems
• Familiarity with LLM orchestration and agentic AI libraries
• Hands on experience with MLOps tools and practices, ensuring seamless integration of machine learning models into production environment

Preferred qualifications, capabilities, and skills

• Familiarity with model fine-tuning techniques such as DPO and RLHF.
• Knowledge of Java, Spark
• Knowledge of financial products and services including trading, investment and risk management