Lead AI Applied ML Engineer

JPMorgan Chase

Confirmed live 2 days ago High trust

Quick summary

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

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Salary context

How this pay compares to similar roles

Similar $219k
$170k most similar roles pay here $272k

This listing doesn't post a salary. Most similar roles pay $192,050–$246,150.

Based on 240 similar postings.

Employer

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 · Lead AI Applied ML Engineer

As the Lead AI Applied ML Engineer, you will lead a team of engineers to design, build, and productionize risk typologies, data pipelines, and supervised and unsupervised machine learning models. You will develop LLM solutions for risk explainability while managing high-volume banking transaction monitoring to detect AML risks, regulatory violations, and behavioral anomalies. Your daily work involves building feedback systems, monitoring for drift or bias, and developing features within Databricks. The role requires expertise in Python, Java, SQL, and NoSQL, alongside experience with PyTorch, TensorFlow, SciKit, NeMo, and Huggingface Transformers. You will integrate models into cloud-scale microservices using AWS infrastructure. This position operates at the intersection of risk modeling and NLP architectures to solve complex problems regarding transaction monitoring, auditability, and automated detection of financial misconduct within a large-scale production environment.

What you'll do

  • Lead and mentor a team of 4 to 5 machine learning engineers.
  • Design, build, and productionize supervised and unsupervised ML models for risk detection.
  • Develop and operate LLM solutions with a focus on risk explainability and auditability.
  • Build and manage high-volume data pipelines for risk features within Databricks.
  • Create feedback and accuracy measurement techniques to evaluate AI solution performance.
  • Monitor production systems to detect and alert on model drift, bias, and degradation.
  • Integrate ML models into cloud-scale, microservices-based architectures using AWS.

What we're looking for

  • 8+ years of experience in cloud-based applications.
  • 4+ years of experience as a Machine Learning Engineer.
  • Expertise in Python and Java for functional programming.
  • Experience with ML frameworks such as PyTorch, TensorFlow, SciKit, NeMo, or Huggingface Transformers.
  • Hands-on experience with AWS services and Databricks.
  • Experience integrating models into cloud-scale, microservices-based architectures.
  • Experience with SQL, NoSQL, and messaging stacks.
  • Operational experience supporting enterprise-grade ML applications in production.

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