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.
Job 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
Required Qualifications, Capabilities, and Skills
- 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
Preferred Qualifications, Capabilities, and Skills
- 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
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