Senior MLOps Engineer - Real-Time ML Pipelines

JPMorgan Chase; Palo Alto (CA); On-site; USD 164,350 - 260,000; Full time

Benefits offered by this job

Health care coverage

On-site health centers

Retirement savings plan

Backup childcare

Tuition reimbursement

Mental health support

Financial coaching

Discretionary incentive compensation

Job summary

JPMorgan Chase in Palo Alto is seeking a Senior MLOps engineer to collaborate with data scientists and build a modern MLOps stack for the Personalization and Insights initiative. You will lead pipelines for distributed training on GPU clusters, real-time and batch inference, and model monitoring in AWS, delivering scalable, low-latency services across consumer banking channels.

This role requires strong engineering fundamentals, cloud experience, and a passion for deploying high-impact ML

Qualifications

• 6+ years of experience in ML engineering, MLOps, or related field.
• Strong Python and cloud (AWS) experience.
• Experience with model quantization and deploying LLMs.
• Experience with distributed training, GPU clusters, and real-time/batch inference.
• Familiarity with monitoring/observability of ML systems.
• Knowledge of vector databases and big data tools.
• Strong collaboration with cross-functional teams.

Responsibilities

• Build, deploy, and maintain robust pipelines for distributed training on GPU-enabled clusters to support scalable machine learning workflows.
• Develop and manage pipelines for high-throughput, real-time inference as well as batch inference, ensuring optimal performance and reliability.
• Implement quantization techniques and deploy large language models (LLMs) to maximize efficiency and resource utilization.
• Oversee the management and optimization of vector databases to support advanced AI and machine learning applications.
• Establish and maintain comprehensive monitoring and observability pipelines to ensure system health, performance, and rapid issue resolution.
• Collaborate with cross-functional teams to integrate new technologies and continuously improve existing infrastructure.
• Partner with product, architecture, and other engineering teams to define scalable and performant technical solutions.

Skills

Python

Cloud computing

AWS

Quantization techniques

Monitoring & observability

Data science fundamentals

Distributed training

Model deployment

Real-time inference

Education

Bachelor's degree in Computer Science or related field

Master's degree in Computer Science or related field

Tools

Docker

Kubernetes

Airflow

Kubeflow

CUDA

GPU clusters

Job description

JPMorgan Chase in Palo Alto is seeking a Senior MLOps engineer to collaborate with data scientists and build a modern MLOps stack for the Personalization and Insights initiative. You will lead pipelines for distributed training on GPU clusters, real-time and batch inference, and model monitoring in AWS, delivering scalable, low-latency services across consumer banking channels.

This role requires strong engineering fundamentals, cloud experience, and a passion for deploying high-impact ML