Applied AI ML Senior 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. 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.