JPMorgan Chase & Co. is seeking a Lead Applied AI & Machine Learning Engineer to join our Corporate Technology Data Science and AI team in Plano, Texas. Build enterprise AI and machine learning solutions that improve decision-making, automate work, and serve internal customers. Take generative AI from concept to production, define semantic consistency across systems, and partner with stakeholders to turn complex business needs into measurable outcomes. You will mentor engineers and data scientists and influence technical direction across Corporate Technology and supported Corporate Functions.

Key responsibilities include: - Build generative AI, agentic AI, and large language model solutions in Python from proof of concept to production with measurable outcomes - Design context engineering approaches to improve model accuracy, latency, reliability, and end-to-end user experience - Lead enterprise semantic modeling strategy, including ontology standards, governance practices, and lifecycle management - Partner with domain experts to create scalable ontologies that represent business entities, relationships, rules, and constraints - Define semantic integration patterns across data pipelines, APIs, data contracts, and experience layers to resolve semantic conflicts - Establish and govern a unified semantic layer that enables trusted analytics across business intelligence, machine learning, and transactional systems - Enable intelligent workflows and AI agents using ontology-driven context, semantic reasoning, and orchestration approaches - Build and maintain pipelines and fra

JPMorgan Chase & Co. is seeking a Lead Applied AI & Machine Learning Engineer to join our Corporate Technology Data Science and AI team in Plano, Texas. Build enterprise AI and machine learning solutions that improve decision-making, automate work, and serve internal customers. Take generative AI from concept to production, define semantic consistency across systems, and partner with stakeholders to turn complex business needs into measurable outcomes. You will mentor engineers and data scientists and influence technical direction across Corporate Technology and supported Corporate Functions.

Key responsibilities include: - Build generative AI, agentic AI, and large language model solutions in Python from proof of concept to production with measurable outcomes - Design context engineering approaches to improve model accuracy, latency, reliability, and end-to-end user experience - Lead enterprise semantic modeling strategy, including ontology standards, governance practices, and lifecycle management - Partner with domain experts to create scalable ontologies that represent business entities, relationships, rules, and constraints - Define semantic integration patterns across data pipelines, APIs, data contracts, and experience layers to resolve semantic conflicts - Establish and govern a unified semantic layer that enables trusted analytics across business intelligence, machine learning, and transactional systems - Enable intelligent workflows and AI agents using ontology-driven context, semantic reasoning, and orchestration approaches - Build and maintain pipelines and fra