Applied AI and ML Lead - Generative AI

JPMorgan Chase & Co.•Jersey City, NJ

17h•$164,350 - $260,000

About The Position

Build what’s next in enterprise AI—solutions 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.

Requirements

Master’s degree in a data science-related discipline and eight years of industry experience, or PhD in a data science-related discipline
Demonstrated experience developing and deploying machine learning and generative AI solutions using Python
Proven ability to write and maintain production-quality code, including documentation and maintainable design patterns
Experience building automated testing practices, including unit tests, and implementing continuous integration pipelines
Experience building and managing data pipelines and processing workflows for analytics and machine learning use cases
Strong scientific thinking and structured problem-solving skills, including hypothesis-driven analysis and metric definition
Strong written and verbal communication skills, with the ability to explain complex concepts to technical and non-technical stakeholders
Demonstrated ownership and attention to detail when operating in ambiguous, complex problem spaces
Ability to work independently while collaborating effectively across product, engineering, data, and business partners

Nice To Haves

Experience designing or governing semantic models and ontologies, including taxonomy design and lifecycle governance
Experience implementing retrieval-augmented generation, tool use, and evaluation strategies for large language model applications
Familiarity with responsible AI techniques, including bias testing, explainability approaches, and model monitoring standards
Experience designing scalable architectures for real-time or near-real-time inference and intelligent workflow orchestration
Experience influencing cross-functional technical direction and mentoring engineers through design reviews and delivery execution

Responsibilities

Build generative AI, agentic AI, and large language model solutions in Python from proof of concept through production deployment 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, application programming interfaces (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 frameworks for model training, evaluation, optimization, monitoring, and machine learning operations
Implement responsible AI practices, model risk controls, and governance aligned to regulated environments
Mentor engineers and data scientists, raising the bar on engineering rigor, reuse, and continuous improvement across the team