Applied AI/ML

This is a unique opportunity to apply your skills and leadership in a dynamic environment, directly impacting the future of Home Lending through innovative AI/ML solutions. You will be at the forefront of technology, shaping the next generation of intelligent products and services at JPMorgan Chase. As Applied AI ML Lead at Consumer & Community Banking Tech, you will drive ML and GenAI projects, leveraging expertise to deliver innovative solutions. Job

. - Design, develop, and deploy state-of-the-art AI/ML/GenAI solutions to meet business objectives. - Architect and implement robust, cloud-native MLOps/LLMOps pipelines and distributed AI/ML infrastructure (AWS, Azure, GCP) for scalable, efficient deployment and monitoring of models in production. - Direct the development and deployment of advanced generative AI solutions (LLMs, RAG, NLP, AI Agents) and classical ML models, integrating state-of-the-art techniques into the ML platform to create innovative fintech products. - Develop advanced monitoring and management tools to ensure high reliability and scalability of AI/ML systems. - Develop and maintain automated pipelines for model deployment, ensuring scalability, reliability, and efficiency. - Implement monitoring mechanisms to track model performance in real-time and ensure model reliability. - Communicate AI/ML capabilities and results to both technical and non-technical audiences. - Build AI Agents and chatbot - Stay informed about the latest trends and advancements in the latest AI/ML research, implement cutting-edge techniques, and leverage external APIs for enhanced functionality.

, capabilities, and skills - Bachelor’s degree or MS or PhD in quantitative discipline, e.g. Computer Science, Mathematics, Operations Research, Data Science. - 5+ years of experience in Machine Learning and Artificial Intelligence engineering. - Experience in applied AI/ML engineering, with a track record of developing and deploying business critical machine learning models in production. - Proficiency in programming languages like Python for model development, experimentation, and integration with OpenAI API. - Extensive hands-on technical experience with machine learning frameworks, libraries, and APIs, such as TensorFlow, PyTorch, Scikit-learn, AWS Bedrock, Transformers, LangChain/LngGraph. - Experience with cloud computing platforms (e.g., AWS, Azure, or Google Cloud Platform), containerization technologies (e.g., Docker and Kubernetes), orchestration tools (Airflow, FastAPI, etc.) and architectural design, implementation, and performance optimization. - Solid understanding of fundamentals of statistics, machine learning (e.g., classification, regression, deep learning, reinforcement learning), and generative model architectures. - Expert in Large Language models (OpenAI, Anthropic, Mistral, etc) including fine-tuning models, prompt engineering, embeddings and context window. - Strong collaboration skills to work effectively with cross-functional teams, communicate complex concepts, and contribute to interdisciplinary projects.