Lead Applied AI/ML Engineer for AML Risk & NLP at Scale

Next Frontier Capital; Jersey City (NJ); On-site; USD 180,000 - 240,000; Full time

Job summary

JPMorgan Chase & Co. in the United States seeks a Lead Applied AI ML Engineer for Global Financial Crimes Tech to design, build, and productionize risk typologies, features, data pipelines, supervised and unsupervised models, and LLM explainability at scale for high-volume banking transactions.

You will lead a team of 4–5 ML engineers and collaborate with Product, SRE and Compliance to continuously improve product adoption, reliability and outcomes in an enterprise-grade setting.

Qualifications

• 8+ years in cloud-based applications with 4+ years as an MLE.
• Strong foundation in Information Retrieval and NLP.
• Proficient in Python and Java.
• Experience integrating models into cloud-scale microservices.
• Hands-on with ML frameworks like PyTorch, TensorFlow, SciKit, NeMo, HuggingFace Transformers.
• Experience with AWS and Databricks.
• Proficient with SQL/NoSQL and messaging systems.
• Excellent communication and ownership in an early-stage environment.
• Operational experience deploying enterprise-grade ML apps.

Responsibilities

• Lead a small group of ML engineers.
• Ensure development and operational standards adherence.
• Design, build, and operate ML models and LLM solutions.
• Develop feedback and accuracy measurement techniques for AI systems.
• Build risk features data pipelines in Databricks.
• Monitor drift, bias, and performance degradation.
• Collaborate with cross-functional teams in agile processes.
• Coordinate with Product, SRE and Compliance to improve adoption and reliability.

Skills

ML leadership

NLP/IR

Python

Java

Databricks

AWS

MLOps

Tools

Databricks

AWS

TensorFlow

PyTorch

Hugging Face

Job description

JPMorgan Chase & Co. in the United States seeks a Lead Applied AI ML Engineer for Global Financial Crimes Tech to design, build, and productionize risk typologies, features, data pipelines, supervised and unsupervised models, and LLM explainability at scale for high-volume banking transactions.

You will lead a team of 4–5 ML engineers and collaborate with Product, SRE and Compliance to continuously improve product adoption, reliability and outcomes in an enterprise-grade setting.