Lead AI/ML Engineer — Risk & NLP at Scale

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

Job summary

JPMorgan Chase & Co. seeks a Lead MLE to design, implement, and productionize AI-driven Global Financial Crimes monitoring at scale across high-volume banking transactions. You will oversee risk modeling, NLP pipelines, and LLM explainability while guiding 4–5 ML engineers in a cross-functional, agile setting.

You will collaborate with product, architecture, data science, SRE and compliance teams to ensure reliable, auditable enterprise ML deployments in a complex financial services environment.

Qualifications

• 8+ years experience in cloud based apps with 4+ years as an MLE.
• Strong foundation in Information Retrieval, Natural Language Processing and
• Expert in Python and JVM languages (Python, Java).
• Experience integrating models into cloud scale, microservices architectures.
• Hands-on with ML frameworks: PyTorch, TensorFlow, SciKit, NeMo, HF Transformers.
• AWS services and Databricks experience; SQL/NoSQL and messaging stacks.
• Excellent communication, bias for action and ownership in early stage env.
• Operational experience in production enterprise ML applications.

Responsibilities

• Lead a small group of ML engineers
• Lead and influence team with development and operational standards adherence
• Design, build, collaborate, and operate ML models
• Design, build and operate LLM solutions
• Design and build feedback/accuracy measurement techniques for AI
• Design, build and operate risk features data pipelines in Databricks
• Monitor drift, bias and performance degradation
• Collaborate with Product Managers, SRE and Compliance SMEs in agile processes

Skills

ML engineering

Python/Java

NLP / IR

MLOps

Production ML

AWS / Databricks

Communication

Tools

Databricks

Docker/Kubernetes

PyTorch / TensorFlow

HuggingFace

Job description

JPMorgan Chase & Co. seeks a Lead MLE to design, implement, and productionize AI-driven Global Financial Crimes monitoring at scale across high-volume banking transactions. You will oversee risk modeling, NLP pipelines, and LLM explainability while guiding 4–5 ML engineers in a cross-functional, agile setting.

You will collaborate with product, architecture, data science, SRE and compliance teams to ensure reliable, auditable enterprise ML deployments in a complex financial services environment.