Applied AI ML Lead
JPMorgan Chase · Posted Oct 13
Global banking, investment, credit card, mortgage, and asset management services
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About the role
The team is building a next generation, AI-driven Surveillance platform that detects regulatory violations, insider risk, misconduct, and behavioral anomalies across enterprise communications and collaboration systems. The work spans risk modeling, NLP and transformer architectures, near real-time inference, and regulatory explainability and auditability. This hands-on senior MLE role designs, builds and productionizes ML and LLM powered detection systems at scale, collaborating with product managers, architects, data science, SRE and compliance teams.
What you will do
- Design LLM powered features such as risk detection, alert explanation, conversation summarization, reviewer assisted co-pilots
- Implement explainability techniques (SHAP, LIME, attention visualization) ensuring model outputs are traceable, versioned and reproducible
- Optimize inference latency and token efficiency for production environments
- Implement RAG and LLM based risk analysis pipelines processing data at web scale
- Bake in augmentation mechanisms leveraging legacy regular expressions for filtering and optimization
- Design real-time and batch processing and scoring pipelines (kafka/spark)
- Implement experiment tracking, model versioning and CI/CD for ML
- 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 used in this role
What the employer is looking for
- 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/Kotlin, Java
- Experience integrating models into cloud scale, microservices based architectures
- Hands-on Databricks experience is required and must include development of production workloads using Spark, Delta Lake, and Databricks Workflows.
- Experience with one or more ML frameworks - Pytorch, Tensorflow, SciKit, NeMo, Huggingface Transformers
- Hands-on experience with AWS services such as SageMaker, ECS, Lambda functions, Bedrock
- 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
- Experience with building production-grade ML pipelines, APIs amd MLOps frameworks such MLflow, Kubeflow
- Experience in surveillance, fraud detection, fintech or risk 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
Benefits and support
- Compensation and benefits are detailed in the job posting
About JPMorgan Chase
JPMorgan Chase & Co. is one of the oldest, largest, and most diversified financial services institutions in the world. Operating globally through the J.P. Morgan and Chase brands, the firm provides a comprehensive range of financial solutions including investment banking, consumer and commercial banking, transaction processing, and asset management. Serving millions of consumers and many of the world's most prominent corporate, institutional, and government clients, the company combines deep market expertise with extensive technological investments.
- Industry
- Banking
- Company size
- 320000+ employees
- Founded
- 1799
- Location
- New York, New York, USA
- Funding stage
- Public Company
Leadership
Chairman and Chief Executive Officer
Chief Financial Officer
Chief Operating Officer
Global Chief Information Officer
CEO, Asset & Wealth Management
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