Applied AI/ML Lead

Job description

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible. 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