Applied AI ML Lead - Generative AI

JPMorgan Chase; Jersey City (NJ); On-site; USD 180,000 - 240,000; Full time

Job summary

JPMorgan Chase is seeking an Applied AI and Machine Learning Lead to translate business needs into production-ready AI solutions within Corporate Technology Data Science and AI.

You will design scalable generative AI systems, establish semantic modeling standards, and partner with stakeholders to deliver measurable business value while mentoring engineers and data scientists across the organization.

Qualifications

• Master's degree in a data science-related discipline with 8+ years of industry experience, or a PhD in a data science-related discipline.
• Proven experience developing and deploying ML and generative AI solutions using Python.
• Production-quality coding skills with documentation and maintainable patterns.
• Experience building automated testing practices and CI pipelines.
• Experience building data pipelines and processing workflows for analytics and ML use cases.
• Strong analytical thinking and problem-solving abilities with metric definition.
• Excellent written and verbal communication, able to explain complex concepts to both technical and non-technical stakeholders.
• Demonstrated ownership and attention to detail in ambiguous, complex spaces.
• Ability to work independently while collaborating across product, engineering, data, and business partners.

Responsibilities

• Build generative AI, agentic AI, and LLM solutions in Python from proof of concept to production with measurable outcomes.
• Design context engineering approaches to improve model accuracy, latency, and reliability.
• Lead enterprise semantic modeling strategy, including ontology standards and governance.
• Collaborate with domain experts to create scalable ontologies representing entities, relationships, rules, and constraints.
• Define semantic integration patterns across data pipelines, APIs, data contracts, and experience layers.
• Establish and govern a unified semantic layer for trusted analytics across BI, ML, and transactional systems.
• Enable intelligent workflows and AI agents using ontology-driven context and semantic reasoning.
• Build and maintain pipelines for model training, evaluation, optimization, monitoring, and ML ops.
• Implement responsible AI practices, model risk controls, and governance in regulated environments.
• Mentor engineers and data scientists to improve engineering rigor and reusable code.

Skills

Python

Machine learning

Generative AI

Semantic modeling

Context engineering

Leadership

Education

Master's degree in data science

PhD in data science

Tools

Python

Job description

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