Applied AI ML Associate

Join a world-class data science team at JPMorgan Chase and help shape the future of our Chief Administrative Office. 

As a Data Scientist Senior Associate 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. 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. 

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 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. 

Required qualifications, capabilities, and skills: 

• Master’s or PhD in Computer Science, Engineering, Mathematics, or a related quantitative field. 
• Minimum 5 years of hands-on experience in applied machine learning, including generative AI, large language models, or foundation models. 
• Experience programming in Python; experience with ML frameworks such as PyTorch or 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, 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 and 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 with building and deploying ML models on cloud platforms such as AWS Sagemaker, EKS, etc. 
• Published research or contributions to open-source GenAI/LLM projects.