Lead AI Applied ML Engineer

JPMorgan Chase & Co.; Jersey City (NJ); On-site; USD 180,000 - 240,000; Full time

Job summary

JPMorgan Chase & Co. in New Jersey seeks a Lead MLE to design, build, and productionize risk-focused ML and LLM solutions for global financial crimes monitoring.

You will lead 4–5 ML engineers and partner with product managers, data scientists, and SREs to deploy scalable models that detect AML risk, regulatory violations, and behavioral anomalies. The role combines risk modeling, NLP architectures, and production-grade engineering with a focus on explainability and auditability.

Qualifications

• 8+ years experience in cloud based applications with 4+ years of experience as an MLE.
• Strong foundation in Information Retrieval, Natural Language Processing and
• Expert in functional programming and JVM based languages- Python, Java

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 and accuracy measurement techniques for AI solutions
• Design, build and operate risk features data pipelines in Databricks
• Conduct monitoring to detect and alert drift, bias and performance degradation
• Work closely within a cross-functional team following agile based processes
• Collaborate closely with Product Managers, SRE and Compliance SMEs to continuously improve product adoption, reliability and outcomes

Skills

Cloud-based apps

Information Retrieval

Natural Language Processing

Python

Java

ML frameworks

PyTorch

TensorFlow

Scikit-learn

AWS

Databricks

SQL

NoSQL

Messaging stacks

MLOps

Production ML

Distributed systems

Communication

Tools

Databricks

AWS

Java Virtual Machine

Job description

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level. We are building a next generation, AI-driven Global Financial Crimes Strategic Monitoring solutions that detects AML risk, regulatory violations, transactions risk, misconduct, and behavioral anomalies.

As a Lead MLE on the team, you will design, build and productionize Risk typologies/ features, data pipelines, supervised and unsupervised ML models, and LLM risk explainability that operate at scale across high-volume banking transactions. You will be responsible for leading a team of 4 to 5 ML engineers. You will work at the intersection of Risk modeling, and NLP architectures, inference systems, regulatory explainability and auditability. This is a hands-on senior role requiring deep expertise in ML operations, LLM integration, scalable ML systems and production grade engineering discipline. This role offers a chance to collaborate with product managers, architects, data science and operational teams, while also engaging in software engineering communities to explore new and emerging technologies.

Job 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 and accuracy measurement techniques for AI solutions
• Design, build and operate risk features data pipelines in Databricks
• Conduct monitoring to detect and alert drift, bias and performance degradation
• Work closely within a cross-functional team following agile based processes
• Collaborate closely with Product Managers, SRE and Compliance SMEs to continuously improve product adoption, reliability and outcomes

Required qualifications, capabilities, and skills

• 8+ years experience in cloud based applications with 4+ years of experience as an MLE
• Strong foundation in Information Retrieval, Natural Language Processing and
• Expert in functional programming and JVM based languages- Python, Java
• Experience integrating models into cloud scale, microservices based architectures
• Experience with one or more ML frameworks - Pytorch, Tensorflow, SciKit, NeMo, Huggingface Transformers
• Hands-on experience with AWS services, and Databricks
• Experience/Exposure to SQL, NoSQL and messaging stacks
• Excellent verbal & written communication skills and bias for action and ownership in early stage env
• Operational experience in supporting an enterprise grade ML application in production

Preferred qualifications, capabilities, and skills

• Knowledge of Firm Databricks CDAO platform is good to have
• Experience with building production-grade ML pipelines, APIs and MLOps frameworks
• Experience in AML, monitoring and investigations systems is a strong plus
• Good understanding of data engineering concepts, distributed systems, and scalable architectures
• Familiarity with vector databases, model serving, and inference optimization is a plus