Applied AI ML Lead Engineer

Join a world-class data science team at JPMorgan Chase and help shape the future of our Chief Administrative Office. As a leader in applied AI and machine learning, you’ll have the opportunity to work on high-impact projects that influence the way we do business across multiple domains. Collaborate with talented colleagues, leverage cutting-edge technologies, and see your work make a tangible difference.

We value curiosity, technical excellence, and a passion for solving complex problems. If you’re ready to accelerate your career and drive meaningful change, we want to hear from you.

As a Applied AI ML VP in the Chief Data & Analytics Office
, you will lead the development and deployment of innovative AI and machine learning solutions. You will collaborate with cross-functional teams to address complex business challenges, drive adoption of modern ML practices, and ensure responsible AI governance. You will have the opportunity to work with state-of-the-art technologies and contribute to a culture of technical excellence and continuous learning.

• Lead the hands‑on design, development, and deployment of advanced AI, GenAI, and large language model solutions.
• Serve as a subject matter expert on a wide range of machine learning techniques and optimizations.
• Collaborate with product, engineering, and business teams to deliver scalable, production‑ready AI systems.
• Conduct experiments using the latest ML technologies, analyze results, and tune models for optimal performance.
• Own end‑to‑end code development in Python for both proof‑of‑concept and production‑ready solutions.
• Integrate generative AI within the ML platform using state‑of‑the‑art techniques.
• Drive adoption of modern ML infrastructure, tools, and best practices.
• Optimize system accuracy and performance by identifying and resolving inefficiencies.
• Communicate technical concepts and results to both technical and business stakeholders.
• Ensure responsible AI practices, model governance, and compliance with regulatory standards.
• Mentor and guide other AI engineers and scientists, fostering a culture of continuous learning.

• Master’s or PhD in Computer Science, Engineering, Mathematics, or a related quantitative field.
• Minimum 8 years of hands‑on experience in applied machine learning, including generative AI, large language models, or foundation models.
• At least 5 years of experience programming in Python; experience with ML frameworks such as PyTorch or Tensor Flow.
• Proven experience designing, training, and deploying large‑scale ML/AI models in production environments.
• Deep understanding of prompt engineering, agentic workflows, and orchestration frameworks.
• Experience with cloud platforms (AWS, Azure, GCP) and distributed systems (Kubernetes, Ray, Slurm).
• Solid grasp of MLOps tools and practices (MLflow, model monitoring, CI/CD for ML).
• Strong communication skills with the ability to explain complex technical concepts to diverse audiences.
• Demonstrated leadership in working effectively with engineers, product managers, and other ML practitioners.
• Experience applying data science and ML techniques to solve business problems.
• Passion for detail, follow‑through, and technical excellence.

• Experience with high‑performance computing and GPU infrastructure (e.g., NVIDIA DCGM, Triton Inference).
• Familiarity with big data processing tools and cloud data services.
• Advanced knowledge in reinforcement learning, meta learning, or related advanced ML areas.
• Experience with search/ranking, recommender systems, or graph techniques.
• Background in financial services or regulated industries.
• Experience with building and deploying ML models on cloud platforms such as AWS Sage Maker, EKS, etc.
• Published research or contributions to open‑source GenAI/LLM projects.