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

As a Applied AI/ML Lead at JPMorgan Chase within Asset and Wealth Management, you will lead efforts to analyze existing processes and sizeable data to design autonomous AI agents. We seek leaders passionate about leveraging advanced data analysis, statistical modeling, and AI/ML techniques to solve complex business challenges through high-quality, cloud-centric software delivery. Our culture thrives on experimentation, continuous improvement, and learning. You will work in a collaborative, trusting, and intellectually stimulating environment—one that values diversity of thought and fosters creative solutions that serve the best interests of our global clientele.

Job responsibilities

Leads the development and implementation of GenAI and Agentic AI solutions using Python to enhance automation and decision-making processes.Oversees the design, deployment, and management of prompt-based models on LLMs for various NLP tasks in the financial services domain.Conducts and guide 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.Collaborates with cross-functional teams to identify requirements and develop solutions to meet business needs within the organization.Communicates effectively with both technical and non-technical stakeholders, including senior leadership.Builds and maintains data pipelines and data processing workflows for prompt engineering on LLMs utilizing cloud services for scalability and efficiency.Develops and maintains tools and frameworks for prompt-based model training, evaluation, and optimization.Analyzes and interprets data to evaluate model performance and identify areas of improvement.

 

Required qualifications, capabilities, and skills

Formal training or certification on software engineering concepts and 5+ years of applied experience.Hands-on experience in building Agentic AI solutions.Experience of LLM orchestration and agentic AI libraries.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 skills 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.Hands-on experience with MLOps tools and practices, ensuring seamless integration of machine learning models into production environments.

 

Preferred qualifications, capabilities, and skills

 

Familiarity with model fine-tuning techniques.Knowledge of financial products and services, including trading, investment, and risk management.