Applied AI ML Associate Senior

As an Applied AI/ML Senior Associate at JPMorgan Chase within the Asset & Wealth Management, you will be a seasoned member of an agile team responsible for designing and delivering trusted data solutions in a secure, stable, and scalable manner. Your role involves developing, testing, and maintaining critical data pipelines and architectures to support the firm's business objectives across various technical areas within different business functions. **Key Responsibilities:** - Efficiently 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 enhance the performance of prompt-based models, utilizing LLM orchestration and agentic AI libraries. - Collaborate with cross-functional teams to identify requirements and develop solutions that meet business needs. - Build and maintain data pipelines and processing workflows for prompt engineering on LLMs, leveraging cloud services for scalability. - Develop and maintain tools and frameworks for prompt-based model training, evaluation, and optimization. - Analyze and interpret data to evaluate model performance and identify areas for improvement. **Qualification Required:** - Formal training or certification on software engineering concepts and a minimum of 3 years of applied experience. - Experience in prompt design and implementation or chatbot application. - Strong programming skills in Python with experience in PyTorch or TensorFlow. - Experience building data pipelines for 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 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 for integrating machine learning models into the production environment. The company prefers candidates with familiarity in model fine-tuning techniques like DPO and RLHF, as well as knowledge of financial products and services including trading, investment, and risk management.,