What are the responsibilities and job description for the AI/ML Engineer – Agentic Private Bank Engineer , Vice President position at JPMorgan Chase?
This is a rare opportunity to help shape the future of our Private Bank. With the sponsorship from the CEO and the heads of the business, our goal is to create an Agentic Private Bank - reimagining the entire process from start to finish, rethinking the operating model including organizational structures and developing AI agents equipped with the latest tools and technologies to fundamentally reshape how we perform this business.
Join our dynamic team of innovators and technologists as an Applied AI/ML Lead, where your mission will be to revolutionize how the Bank services and advises clients, deepen client engagements, and drive process transformation. You will lead efforts to analyze existing processes and vast amounts of 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
- Lead the development and implementation of GenAI and Agentic AI solutions using Python to enhance automation and decision-making processes.
- Oversee the design, deployment, and management of prompt-based models on LLMs for various NLP tasks in the financial services domain.
- Conduct 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.
- 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, including senior leadership.
- 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 frameworks for prompt-based model training, evaluation, and optimization.
- Analyze and interpret data to evaluate model performance and identify areas of improvement.
Required qualifications, capabilities, and skills
- PhD in a quantitative discipline, e.g. Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science Or an MS with at least 7 years of industry or research experience in the field.
- Hands-on experience in building Agentic AI solutions.
- Familiarity with 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.