**Applied AI and ML Lead - Generative AI** at JPMorgan Chase. Design, build, and deploy AI solutions in Python, from PoC to production. Collaborate with stakeholders to translate complex needs into measurable outcomes, ensuring semantic consistency across systems. Establish enterprise semantic modeling strategy and govern a unified semantic layer. Lead teams in mentoring, governance, and cross-functional technical direction.
Full Job Description
Location: Jersey City, NJ, United States
Build whats next in enterprise AIsolutions that materially improve how teams make decisions, automate work, and serve internal customers. You will take generative AI from concept to production, help set the standard for semantic consistency across systems, and partner closely with stakeholders to turn complex business needs into measurable outcomes. You will mentor talent and influence technical direction across Corporate Technology and supported Corporate Functions.
As an Applied AI and Machine Learning Lead at JPMorganChase within Corporate Technology Data Science and AI, you will design, build, and deploy scalable analytical and generative AI solutions that deliver measurable business value. You will translate complex business needs into clear problem statements, success metrics, and production-ready models and intelligent workflows. You will help establish semantic modeling standards and a unified semantic layer that improves trust and consistency across analytics and AI use cases.
Job Responsibilities
Build generative AI, agentic AI, and large language model solutions in Python from proof of concept through production deployment with measurable outcomesDesign context engineering approaches to improve model accuracy, latency, reliability, and end-to-end user experienceLead enterprise semantic modeling strategy, including ontology standards, governance practices, and lifecycle managementPartner with domain experts to create scalable ontologies that represent business entities, relationships, rules, and constraintsDefine semantic integration patterns across data pipelines, application programming interfaces (APIs), data contracts, and experience layers to resolve semantic conflictsEstablish and govern a unified semantic layer that enables trusted analytics across business intelligence, machine learning, and transactional systemsEnable intelligent workflows and AI agents using ontology-driven context, semantic reasoning, and orchestration approachesBuild and maintain pipelines and frameworks for model training, evaluation, optimization, monitoring, and machine learning operationsImplement responsible AI practices, model risk controls, and governance aligned to regulated environmentsMentor engineers and data scientists, raising the bar on engineering rigor, reuse, and continuous improvement across the teamRequired Qualifications, Capabilities, and Skills
Masters degree in a data science-related discipline and eight years of industry experience, or PhD in a data science-related disciplineDemonstrated experience developing and deploying machine learning and generative AI solutions using PythonProven ability to write and maintain production-quality code, including documentation and maintainable design patternsExperience building automated testing practices, including unit tests, and implementing continuous integration pipelinesExperience building and managing data pipelines and processing workflows for analytics and machine learning use casesStrong scientific thinking and structured problem-solving skills, including hypothesis-driven analysis and metric definitionStrong written and verbal communication skills, with the ability to explain complex concepts to technical and non-technical stakeholdersDemonstrated ownership and attention to detail when operating in ambiguous, complex problem spacesAbility to work independently while collaborating effectively across product, engineering, data, and business partnersPreferred Qualifications, Capabilities, and Skills
Experience designing or governing semantic models and ontologies, including taxonomy design and lifecycle governanceExperience implementing retrieval-augmented generation, tool use, and evaluation strategies for large language model applicationsFamiliarity with responsible AI techniques, including bias testing, explainability approaches, and model monitoring standardsExperience designing scalable architectures for real-time or near-real-time inference and intelligent workflow orchestrationExperience influencing cross-functional technical direction and mentoring engineers through design reviews and delivery execution#LI-RB3
Lead applied AI and generative AI solutions from concept to production, setting enterprise semantic standards.
