Lead ML Engineer, MLOps & Real-Time Inference

JP Morgan Chase; Palo Alto (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

JP Morgan Chase seeks a Senior MLOps engineer to collaborate with data scientists on an advanced MLOps stack for personalisation and insights. You will build and maintain pipelines for distributed training, real-time and batch inference, and model deployment on AWS within a controlled environment.

The role emphasizes scalable pipelines, monitoring, and cross-functional collaboration with product and architecture teams to deliver performant ML solutions in a modern cloud stack.

Qualifications

• BS or MS in Computer Science or related field with 4+ years (MS) or 6+ years (BS).
• Strong Python proficiency and data science fundamentals.
• Experience with monitoring/observability tools for model inputs/outputs and feature stats.
• Operational experience with big data/ML tools such as Ray, Spark.

Responsibilities

• Build, deploy, and maintain pipelines for distributed training on GPU clusters.
• Develop real-time and batch inference pipelines with high throughput.
• Implement quantization and deploy large language models to maximize efficiency.
• Manage vector databases for AI/ML applications.
• Establish comprehensive monitoring and observability for system health.

Skills

Python

Data science

Monitoring/observability

Iterative development

Education

BS in Computer Science or related Engineering

MS in Computer Science or related Engineering

Tools

Docker

Kubernetes

Airflow

Kubeflow

Ray

DuckDB

Spark

Job description

JP Morgan Chase seeks a Senior MLOps engineer to collaborate with data scientists on an advanced MLOps stack for personalisation and insights. You will build and maintain pipelines for distributed training, real-time and batch inference, and model deployment on AWS within a controlled environment.

The role emphasizes scalable pipelines, monitoring, and cross-functional collaboration with product and architecture teams to deliver performant ML solutions in a modern cloud stack.