JPMorgan Chase—Applied AI ML Associate Senior

Job description

Description: Shape the future of intelligent products by building agent-driven systems that solve meaningful business problems at scale. Join a collaborative team where you will deepen your expertise in modern machine learning, software engineering, and responsible automation while delivering measurable impact. Bring your Python and applied machine learning skills to a role with strong career growth, mobility, and end-to-end ownership. As an Applied AI ML Associate Senior at JPMorgan Chase, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products using AI/ML technologies in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives. You will need to leverage your strong knowledge of ML, NLP, Deep Learning, LLM, and experience in working with massive amounts of data to build systems that reach JP Morgan scale. Job responsibilities - Hands-on design, development, and deployment of advanced AI, GenAI, and Agentic business solutions. - Partner with engineering leads and architecture to implement, test & deploy multi-agentic AI solutions with prompt/tool orchestration, retrieval, memory strategies, guardrails, and conversation state management. - Implement robust evaluation for agentic systems, offline test suites, golden datasets, regression tests, latency & cost tracking, and human-in-the-loop review. - Develop end-to-end ML pipelines necessary to transform existing applications and business processes into true AI systems. Implement Retrieval-Augmented Generation methods to enhance the LLM's ability to retrieve and generate accurate answers from large datasets. - Serve as a subject matter expert on a wide range of machine learning techniques and optimizations. - Use Deep Learning frameworks like CNN, RNN, LSTM and Attention for solving use cases requiring semantic search, named entity resolution, forecasting, anomaly detection among many other techniques. - Own small-to-medium engineering deliverables end-to-end: requirements clarification, design approach, implementation, testing, release, and post-release support. - You will collaborate to develop large-scale data modeling experiments, evaluating against strong baselines, and extracting key statistical insights and/or cause and effect relations. - Utilize Prompt Engineering techniques to fine-tune and optimize LLMs for specific use cases and improve response accuracy and relevance. Required qualifications, capabilities, and skills - Advanced Degree in field of Computer Science, Data Science or equivalent discipline - 4+ years of working experience as a hands-on ML Engineer/Data Engineer/Data Scientist, - Experience with machine learning techniques and advanced analytics (e.g. regression, classification, clustering, time series, econometrics, causal inference, mathematical optimization) - Experience in understanding end-user requirements, designing and building Agentic AI solutions in production. - Hands on experience in designing & building AI Tools, using Agentic frameworks, Google ADK, LangGraph, A2A, Knowledge Graphs, eval tooling. - Hands on expertise with Python, PySpark, - Strong communication skills along with significant experience of managing stakeholder of diverse background. Preferred qualifications, capabilities, and skills - Experience in building multi-agent enterprise solutions is preferred - Experience in driving Data Science projects end to end is preferred - Experience in large scale Machine Learning system design is preferred - Experience working with end-to-end pipelines consisting of Cloud services is preferred - Experience with AWS Cloud/ML ecosystem is good to have. - DL frameworks like TensorFlow/PyTorch over GPU is preferred, BERT, SBERT, etc.