Senior Applied AI ML Engineer

JPMorgan Chase & Co.; Jersey City (NJ); On-site; USD 150,000 - 210,000; Full time

Job summary

JPMorgan Chase & Co. in Jersey City, NJ is seeking a Senior ML Engineer for AI-driven financial crimes monitoring. This hands-on role focuses on scalable, production-ready ML systems and NLP/IR capabilities.

You will design and productionize risk models, data pipelines in Databricks, and LLM-based explainability, collaborating with product managers, SRE and compliance teams to deliver robust solutions.

Qualifications

• Advanced degree in CS/DS/AI with relevant industry experience.
• Strong NLP, IR, and production ML expertise.
• Proficient in Python and JVM languages (Python, Java).
• Experience delivering cloud-scale ML solutions and ML Ops.

Responsibilities

• Design, build, and productionize ML models and features.
• Develop LLM-based risk solutions and explainability.
• Create scalable data pipelines in Databricks.
• Monitor models for drift, bias, and performance.
• Collaborate with product, SRE, and compliance teams.

Skills

Natural Language Processing

Information Retrieval

Python

Java

ML Model Deployment

LLM Explainability

Education

PhD in Computer Science, Data Science, AI or similar fields

MS in Computer Science, Data Science, AI or similar fields

BS in Computer Science, Data Science, AI or similar fields

Tools

Databricks

AWS

TensorFlow

PyTorch

HuggingFace Transformers

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 Senior 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 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

• 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

• PhD in Computer Science, Data Science, AI or similar fields with with 2+ years of experience Or MS in Computer Science, Data Science, AI or similar fields with with 4+ years of experience or BS in Computer Science, Data Science, AI or similar fields with with 8+ years of experience
• 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