Applied AI/ML Lead

JOB DESCRIPTION

The GT CDAO team is an elite machine learning group strategically located within the Chief Technology Office of JP Morgan Chase. GT CDAO tackle business critical priorities using innovative machine learning techniques and technologies with a focus on machine learning for Software, Cybersecurity and Technology Infrastructure. The team partners closely with stakeholders in these areas to execute projects that require Generative AI and machine learning development to support JPMC businesses as they grow.

Strategically positioned in the Chief Technology Office, our work spans across Cybersecurity, Global Technology Infrastructure and the Software Development Lifecycle (SDLC). With this unparalleled access to technology groups in the firm, the role offers a unique opportunity to explore novel and complex challenges that could profoundly transform how the bank operates.

As an Applied AI/ML Lead, you will apply sophisticated machine learning methods to a wide variety of complex tasks including data mining , exploratory data analysis and visualization, text understanding and embedding, anomaly detection in time series and log data, large language models (LLMs) and generative AI, reinforcement learning and recommendation systems. You must excel in working in a highly collaborative environment together with the business, technologists and control partners to deploy solutions into production. You must also have a passion for AI and machine learning and invest independent time towards learning, researching and experimenting with new innovations in the field. You must have solid expertise in Deep Learning with hands-on implementation experience and possess strong analytical thinking, a deep desire to learn and be highly motivated.

AI Engineering (Production & MLOps): In addition to applied research, ML specialists in this role are expected to operate as AI engineers—designing, building, and maintaining production-ready ML/GenAI systems. This includes writing high-quality, well-tested code; applying strong software engineering practices (version control, code reviews, documentation, modular design, and secure coding); and partnering with platform and engineering teams to implement CI/CD, reproducible training/inference pipelines, model/version governance, performance optimization, monitoring/alerting, and reliable deployment patterns across batch and real-time use cases.

Job Responsibilities

• Build and maintain production-ready AI/ML services and pipelines by applying best-in-class software engineering practices (clean, modular code; testing; code reviews; documentation; CI/CD), and ensuring robust deployment, monitoring, and ongoing performance/reliability of models in production.
• Develop state-of-the art machine learning models to solve real-world problems and apply it to complex business critical problems in Cybersecurity, Software and Technology Infrastructure
• Collaborate with multiple partner teams in Cybersecurity, Software and Technology Infrastructure to deploy solutions into production
• Drive firmwide initiatives by developing large-scale frameworks to accelerate the application of machine learning models across different areas of the business
• Contribute to reusable code and components that are shared internally and also externally
• Research and explore new machine learning methods through independent study, attending industry-leading conferences and experimentation

Required qualifications, capabilities and skills

• PhD or MSc in a quantitative discipline (e.g. Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science.)
• Extensive experience with large language models (LLMs) and accompanying tools & techniques in the LLM ecosystem (e.g. LangChain, LangGraph, Vector databases, opensource Models, RAG, Agentic Systems & Workflows, LLM fine-tuning)
• Strong AI Engineering skills and experience - core technical, software, and application-building skills to design, deploy, and maintain reliable artificial intelligence systems.
• Hands-on experience and solid understanding of machine learning and deep learning methods
• Extensive experience with machine learning and deep learning toolkits (e.g.: TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas)
• Scientific thinking and the ability to invent
• Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performan…