J.P. Morgan is a global leader in financial services, providing strategic advice and products to the world’s most prominent corporations, governments, wealthy individuals and institutional investors. Our first-class business in a first-class way approach to serving clients drives everything we do. We strive to build trusted, long-term partnerships to help our clients achieve their business objectives.

As a Machine Learning Software Engineer within JPMorgan, you will be a vital member of an agile team, tasked with designing and delivering secure, stable, and scalable market-leading technology products. Your role involves implementing critical technology solutions across a variety of technical areas within different business functions, all in support of the firm's business objectives.

Job responsibilities

Work with product managers, data scientists, ML engineers, and other stakeholders to understand requirements. Design, develop, and deploy state-of-the-art AI/ML/LLM/GenAI solutions to meet business objectives. Develop and maintain automated pipelines for model deployment, ensuring scalability, reliability, and efficiency. Implement optimization strategies to fine-tune generative models for specific NLP use cases, ensuring high-quality outputs in summarization and text generation. Conduct thorough evaluations of generative models (e.g., GPT-4), iterate on model architectures, and implement improvements to enhance overall performance in NLP applications. Implement monitoring mechanisms to track model performance in real-time and ensure model reliability. Communicate AI/ML/LLM/GenAI capabilities and results to both technical and non-technical audiences. 

Required qualifications, capabilities, and skills

Bachelor's or Master's degree in Computer Science, Engineering, or a related field Minimum 3 years of demonstrated 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. Experience with machine learning frameworks, libraries, and APIs, such as TensorFlow, PyTorch, Scikit-learn, and OpenAI API. Experience with cloud computing platforms (e.g., AWS, Azure, or Google Cloud Platform), containerization technologies (e.g., Docker and Kubernetes), and microservices design, implementation, and performance optimization. Solid understanding of fundamentals of statistics, machine learning (e.g., classification, regression, time series, deep learning, reinforcement learning), and generative model architectures, particularly GANs, VAEs. Ability to identify and address AI/ML/LLM/GenAI challenges, implement optimizations and fine-tune models for optimal performance in NLP applications. Strong collaboration skills to work effectively with cross-functional teams, communicate complex concepts, and contribute to interdisciplinary projects.

 

Preferred qualifications, capabilities, and skills

Familiarity with the financial services industries. 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. A portfolio showcasing successful applications of generative models in NLP projects, including examples of utilizing OpenAI APIs for prompt engineering.