Senior Applied AI ML Engineer

JPMorgan Chase

Confirmed live today High trust

Quick summary

Work type
On-site
Location
Jersey City, NJColumbus, OH
Employment
Full-time
Posted
55 days ago
Freshness
Confirmed live today

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Salary context

How this pay compares to similar roles

Similar $216k
$158k $290k
below market most similar roles pay here above market

This listing doesn't post a salary. Most similar roles pay $177,737–$254,750.

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 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 · Senior Applied AI ML Engineer

The Senior Applied AI ML Engineer joins the team to develop next-generation, AI-driven Global Financial Crimes Strategic Monitoring solutions. This role focuses on detecting AML risk, regulatory violations, transaction risk, misconduct, and behavioral anomalies. You will design, build, and productionize risk typologies, data pipelines, supervised and unsupervised ML models, and LLM risk explainability for high-volume banking transactions. Daily tasks include building feedback and accuracy measurement techniques, operating risk features in Databricks, and monitoring for drift, bias, and performance degradation. You must possess expertise in Python, Java, PyTorch, TensorFlow, SciKit, NeMo, and Huggingface Transformers. The role requires integrating models into cloud-scale microservices using AWS, SQL, and NoSQL. You will solve complex problems at the intersection of Risk modeling, NLP architectures, and regulatory auditability within an agile environment.

What you'll do

  • Design, build, and operate supervised and unsupervised ML models for risk detection.
  • Develop and productionize LLM solutions and risk explainability features.
  • Build and operate risk feature data pipelines using Databricks.
  • Create feedback and accuracy measurement techniques for AI solutions.
  • Monitor systems to detect and alert on model drift, bias, and performance degradation.
  • Integrate ML models into cloud-scale, microservices-based architectures.
  • Support and maintain enterprise-grade ML applications in a production environment.

What we're looking for

  • PhD in Computer Science, Data Science, AI or similar with 2+ years of experience.
  • MS in Computer Science, Data Science, AI or similar with 4+ years of experience.
  • BS in Computer Science, Data Science, AI or similar with 8+ years of experience.
  • Strong foundation in Information Retrieval and Natural Language Processing.
  • Expert in functional programming and JVM based languages including Python and Java.
  • Experience integrating models into cloud scale, microservices based architectures.
  • Experience with ML frameworks such as Pytorch, Tensorflow, SciKit, NeMo, or Huggingface Transformers.
  • Hands-on experience with AWS services, Databricks, SQL, NoSQL, and messaging stacks.
  • Operational experience supporting an enterprise grade ML application in production.
  • Knowledge of Firm Databricks CDAO platform (preferred).
  • Experience building production-grade ML pipelines, APIs, and MLOps frameworks (preferred).
  • Experience in AML, monitoring, and investigations systems (preferred).
  • Understanding of data engineering concepts, distributed systems, and scalable architectures (preferred).
  • Familiarity with vector databases, model serving, and inference optimization (preferred).

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