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 learningyoullhave the opportunity to work on high-impact projects that influence the way we do business across multiple domains. Collaborate with talented colleaguesleveragecutting-edgetechnologies and see your work make a tangible difference. We value curiosity technical excellence and a passion for solving complex problems. Ifyoureready to accelerate your career and drive meaningful change we want to hear from you.

Job Summary:
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 withstate-of-the-arttechnologies and contribute to a culture of technical excellence and continuous learning.

Job Responsibilities:

• 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 foroptimalperformance.
• Own end-to-end code development in Python for both proof-of-concept and production-ready solutions.
• Integrate generative AI within the ML platform usingstate-of-the-arttechniques.
• Drive adoption of modern ML infrastructure tools and best practices.
• Optimizesystem accuracy and performance byidentifyingand 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.

Required Qualifications Capabilities and Skills:

• Masters 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 asPyTorchor TensorFlow.
• 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 RaySlurm).
• Solid grasp ofMLOpstools and practices (MLflow model monitoring CI/CD for ML).
• Strong communicationskills 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.

Preferred Qualifications Capabilities and Skills:

• 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 withbuilding and deploying ML models on cloud platforms such as AWSSagemaker EKS etc.
• Published research or contributions to open-source GenAI/LLM projects.