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.
- Work with product managers, data scientists, ML engineers, and other stakeholders to understand requirements.
- 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.
- 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.
- 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.
- Expertise in designing and implementing pipelines using Retrieval-Augmented Generation (RAG).
- Hands-on knowledge of Chain-of-Thoughts, Tree-of-Thoughts, Graph-of-Thoughts prompting strategies.
- Familiarity with ethical AI, including bias mitigation, explainability and escalation protocols for risky outputs.