Applied AI ML Lead, Generative AI and Semantic Modeling

JPMorgan Chase

Confirmed live yesterday Trusted

Quick summary

Work type: On-site
Location: Jersey City, NJ
Employment: Full-time
Posted: 105 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

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Similar $220k

$148k $289k

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This listing doesn't post a salary. Most similar roles pay $184,900–$254,750.

Based on 240 similar postings.

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About JPMorgan Chase

JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.

JPMorgan Chase currently has 1226 open roles on FindRole.

Listed pay typically runs $156,750–$215,000 across 5 roles with salary data.

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At a glance

TL;DR · Applied AI ML Lead, Generative AI and Semantic Modeling

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As an Applied AI ML Lead - Generative AI and Semantic Modeling within Corporate Technology Data Science and AI, you will design, build, and deploy scalable analytical and generative AI solutions. You will work with a collaborative team to turn complex business problems into production-ready models and intelligent workflows. Your daily responsibilities include developing large language model solutions, designing context engineering approaches, and leading semantic modeling strategies to establish ontology standards. You will build pipelines for model training, evaluation, and monitoring while implementing responsible AI practices in regulated environments. You must utilize Python to write production-quality code and manage data processing workflows. Key technical focus areas include semantic integration patterns across APIs and data contracts, orchestration methods for AI agents, and establishing a unified semantic layer for trusted analytics.

Skills

Python Generative AI large language model s Machine Learning Data Pipelines Semantic Modeling Ontology Design Context Engineering Data Contracts Responsible AI

What you'll do

Develop generative AI, agent-based AI, and large language model solutions in Python from proof of concept to production.
Design context engineering approaches to improve model accuracy, latency, and reliability in real-world workflows.
Lead semantic modeling strategy, including ontology standards, governance, and lifecycle management for enterprise needs.
Create scalable ontologies that model business entities, relationships, rules, and constraints with domain experts.
Build and govern a unified semantic layer to enable trusted analytics across business intelligence and machine learning systems.
Enable intelligent workflows and AI agents using ontology-driven context, semantic reasoning, and orchestration methods.
Build and maintain pipelines for model training, evaluation, optimization, monitoring, and production operations.
Implement responsible AI practices and model risk controls aligned with regulated environments and internal standards.

What we're looking for

Master’s degree in a data science-related discipline and 8 years of industry experience, or a PhD in a data science-related discipline.
Hands-on experience developing and deploying machine learning and generative AI solutions using Python.
Demonstrated ability to write and maintain production-quality code with considerations for reliability, performance, and maintainability.
Experience with continuous integration practices and unit test development to support quality delivery.
Experience building and managing data pipelines and processing workflows for analytical and machine learning use cases.
Strong written and verbal communication skills to translate technical decisions into business impact.
Demonstrated scientific thinking and structured problem-solving skills for ambiguous, data-driven challenges.
Experience building LLM applications using context engineering, semantic modeling, and ontology design (preferred).

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