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.

As an Applied AI ML Lead Engineer 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.

#LI-RB3