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. 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 6 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.