Applied AI ML Lead, Generative AI and Semantic Modeling

JPMorgan Chase

Confirmed live 2 days ago High trust

Quick summary

Work type
On-site
Location
Jersey City, NJ
Posted
59 days ago
Freshness
Confirmed live 2 days ago

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How this pay compares to similar roles

Similar $218k
$175k most similar roles pay here $272k

This listing doesn't post a salary. Most similar roles pay $189,737–$246,300.

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 1117 open roles on FindRole.

Listed pay typically runs $186,160–$215,000 across 7 roles with salary data.

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At a glance

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

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 Python-based large language model solutions, designing context engineering approaches for improved performance, and leading semantic modeling strategies including ontology standards and governance. You will build unified semantic layers to ensure consistency across analytics and AI use cases while managing pipelines for model training, evaluation, and monitoring. The role requires expertise in Python, production-quality code, and continuous integration practices. You will solve complex problems involving semantic reasoning, data contracts, and responsible AI practices within a regulated environment to deliver measurable business value through advanced machine learning.

What you'll do

  • Develop generative AI, agent-based AI, and LLM 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 BI, ML, and transactional systems.
  • Enable intelligent workflows and AI agents using ontology-driven context and orchestration methods.
  • Build and maintain pipelines for model training, evaluation, optimization, monitoring, and production operations.
  • Implement responsible AI practices and 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 that support 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 with LLM context engineering, semantic modeling, or mentoring others (preferred).

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