JPMorgan Chase
Applied AI ML Lead - Generative AI
$171K–$260K
Jersey City, NJ +1 · Full-time · Posted Oct 13, 2026
- Location
- Jersey City, NJ
Plano, TX - Experience
- Eight years of industry experience with a master’s degree; alternatively, a PhD in a data science-related discipline.
- Education
- Master’s degree in a data science-related discipline, or PhD in a data science-related discipline.
About the job
Leads the design and production deployment of generative AI and machine learning solutions, while establishing enterprise semantic modeling standards and guiding technical direction across Corporate Technology and supported Corporate Functions.
What you'll do
- Build and deploy generative AI, agentic AI, and large language model solutions; develop context-engineering approaches and intelligent workflows.
- Lead enterprise semantic modeling, ontology governance, and unified semantic-layer strategy across analytics and AI systems.
- Build model training, evaluation, optimization, and monitoring pipelines; implement responsible AI controls and mentor engineers and data scientists.
What they ask for
- Master’s degree in a data science-related discipline and eight years of industry experience, or a PhD in a data science-related discipline.
- Demonstrated experience developing and deploying machine learning and generative AI solutions using Python.
- Experience writing and maintaining production-quality code, building automated tests including unit tests, and implementing continuous integration pipelines.
- Experience building and managing data pipelines and processing workflows for analytics and machine learning.
Nice to have
- Experience designing or governing semantic models and ontologies, including taxonomy design and lifecycle governance.
- Experience with retrieval-augmented generation, tool use, and evaluation strategies for large language model applications.
Pay
USD 171,000.00-$260,000.00 base salary
Tools
Python, APIs
AI focus
Develops generative AI, agentic AI, and large language model solutions; applies context engineering, ontology-driven context, semantic reasoning, orchestration, and responsible AI practices.
Model-written summary · the original posting is authoritative