Applied AI ML Lead

Role Overview: You will be part of the Digital Intelligence team at JPMorgan, working as an Applied AI ML Lead. Your main responsibility will be to collaborate with various business lines and functions to deliver impactful software solutions in the financial services industry. You will have the opportunity to research, experiment, develop, and implement high-quality machine learning models, services, and platforms to drive significant business impact. Additionally, you will be involved in designing and implementing scalable data processing pipelines and analyzing insights to optimize business results. Key Responsibilities: - Design, deploy, and manage prompt-based models on Large Language Models (LLMs) for various Natural Language Processing (NLP) tasks in the financial services domain. - Conduct research on prompt engineering techniques to enhance the performance of prompt-based models in the financial services field, utilizing LLM orchestration and agentic AI libraries. - Collaborate with cross-functional teams to understand requirements and develop solutions to address organizational needs. - Communicate effectively with technical and non-technical stakeholders. - Build and maintain data pipelines and processing workflows for prompt engineering on LLMs using cloud services for scalability and efficiency. - Develop and maintain tools and frameworks for prompt-based model training, evaluation, and optimization. - Analyze and interpret data to assess model performance and identify areas for enhancement. Qualifications Required: - Formal training or certification in software engineering concepts with at least 5 years of applied experience. - Experience in prompt design and implementation or chatbot applications. - Strong programming skills in Python, with proficiency in PyTorch or TensorFlow. - Experience in building data pipelines for structured and unstructured data processing. - Proficiency in developing APIs and integrating NLP or LLM models into software applications. - Hands-on experience with cloud platforms such as AWS or Azure for AI/ML deployment and data processing. - Excellent problem-solving skills and the ability to communicate ideas and results clearly to stakeholders and leadership. - 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 to ensure seamless integration of machine learning models into the production environment.,