As a Senior Associate Software Engineer within JPMorgan Chase Digital Technology, you will lead the development and innovation of agentic applications using LLM technologies. You will work closely with a talented team to design scalable, resilient applications with strong observability, contributing to JP Morgan Chase's mission of delivering exceptional self-service solutions. Your role will be pivotal in driving innovation and enhancing the user experience, making a difference in the lives of our clients and the wider community.

Job Responsibilities

• Develop solutions related to data architecture, ML Platform as well as GenAI platform architecture, provide tactical solution and design support to the team and embedded with engineering on  the execution and implementation of processes and procedures
• Serve as a subject matter expert on a wide range of ML techniques and optimizations.
• Provide in-depth knowledge of distributed ML platform deployment including training and serving.
• Create curative solutions using GenAI workflows through advanced proficiency in large language models (LLMs) and related techniques.
• Gain Experience with creating a Generative AI evaluation and feedback loop for GenAI/ML pipelines.
• Get Hands on code and design to bring the experimental results into production solutions by collaborating with engineering team. 
• Own end to end code development in python/Java for both proof of concept/experimentation and production-ready solutions.
• Optimize system accuracy and performance by identifying and resolving inefficiencies and bottlenecks and collaborate with product and engineering teams to deliver tailored, science and technology-driven solutions.
• Drives decisions that influence the product design, application functionality, and technical operations and processes.

Required Qualifications, Capabilities, and Skills

• Formal training or certification on AI ML concepts and 3+ years applied experience 
• Solid understanding of using ML techniques specially in Natural Language Processing (NLP) and Large Language Models (LLMs)
• Hands-on experience with machine learning and deep learning methods.
• Good understanding in deep learning frameworks such as PyTorch or TensorFlow.
• Experience in advanced applied ML areas such as GPU optimization, finetuning, embedding models, inferencing, prompt engineering, evaluation, RAG (Similarity Search).
• Deep understanding of Large Language Model (LLM) techniques, including Agents, Planning, Reasoning, and other related methods.
• Practical cloud native experience such as AWS

Preferred Qualifications, Capabilities, and Skills

• Experience with Ray, MLFlow, and/or other distributed training frameworks.
• In-depth understanding of Embedding based  Search/Ranking, Recommender systems, Graph techniques, and other advanced methodologies.
• Experience with building and deploying ML models on cloud platforms such as AWS and AWS tools like Sagemaker.
• Exposure to agentic frameworks such as Langchain, Langgraph, RASA, Parlant, Decagon.