Applied AI ML Engineer Director - NLP​/LLM and Graphs

Position: Applied AI ML Engineer Director - NLP / LLM and Graphs
The Chief Data & Analytics Office (CDAO) at JPMorgan Chase is responsible for accelerating the firm’s data and analytics journey. This includes ensuring the quality, integrity, and security of the company's data, as well as leveraging this data to generate insights and drive decision-making. The CDAO is also responsible for developing and implementing solutions that support the firm’s commercial goals by harnessing artificial intelligence and machine learning technologies to develop new products, improve productivity, and enhance risk management effectively and responsibly.

As an Applied AI ML Director - NLP / LLM and Graphs within the Chief Data & Analytics Office, Machine Learning Centre of Excellence, you will have the opportunity to apply sophisticated machine learning methods to complex tasks including natural language processing, graph analytics, speech analytics, time series, reinforcement learning and recommendation systems. You will collaborate with various teams and actively participate in our knowledge sharing community.

We are looking for someone who excels in a highly collaborative environment, working together with our business, technologists and control partners to deploy solutions into production. If you have a strong passion for machine learning and enjoy investing time towards learning, researching and experimenting with new innovations in the field, this role is for you. We value solid expertise in Deep Learning with hands-on implementation experience, strong analytical thinking, a deep desire to learn and high motivation.

Job Responsibilities Develop state-of-the art machine learning models to solve real-world problems and apply it to tasks such as natural language processing (NLP), speech recognition and analytics, time-series predictions or recommendation systems

Collaborate with multiple partner teams such as Business, Technology, Product Management, Legal, Compliance, Strategy and Business Management to deploy solutions into production

Lead, mentor, and inspire a team of AI engineers, fostering a culture of excellence, innovation, and continuous learning Drive Firm wide initiatives by developing large-scale frameworks to accelerate the application of machine learning models across different areas of the business

Research and explore new machine learning methods through independent study, attending industry-leading conferences, experimentation and participating in our knowledge sharing community 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 significant years of industry or research experience in the field.

Solid background in NLP, LLM and graph analytics and hands-on experience and solid understanding of machine learning and deep learning methods

Hands on experience building agentic AI / multi-agent systems within regulated or compliance-driven environments

Recent hands-on experience training, deploying models and pipelines

Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals, with a focus on agentic systems and LLM evaluation

Experience with big data and scalable model training and solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences.

Scientific thinking with the ability to invent and to work both independently and in highly collaborative team environments

Solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences. Curious, hardworking and detail-oriented, and motivated by complex analytical problems

Preferred qualifications, capabilities , and skills:

Strong background in Mathematics and Statistics and familiarity with the financial services industries and continuous integration models and unit test development

Knowledge in graph integration with LLM, Reinforcement Learning or Meta Learning

Experience with A/B experimentation and data/metric-driven…