Senior MLOps Director – Real-Time ML & Personalization

JPMorgan Chase & Co.; Palo Alto (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

JPMorgan Chase & Co. seeks a Senior MLOps Engineer (Executive Director) to collaborate with data scientists on building and deploying ML models on a modern MLOps stack. You will lead fine-tuning, RL, real-time and batch serving, and scale training on GPU clusters in AWS.

This role powers Personalization and Insights across Chase channels with high throughput, low latency experiences. You will work within a controlled environment with production validation, observability, and cross-functional

Qualifications

• BS in Computer Science or related Engineering with 10+ years experience or MS with 6+ years experience.
• Strong Python or cloud/AWS background.
• Understanding of LLM quantization techniques (PTQ, AWQ).
• Experience with Transformer models and large-scale model serving.
• Experience in ML training and reinforcement learning methods.
• Systems engineering fundamentals: caching, autoscaling, high throughput, low latency.

Responsibilities

• Build, deploy, and maintain pipelines for distributed training on GPU clusters.
• Develop real-time and batch inference systems for high throughput and low latency.
• Deploy quantization techniques and open-weight LLMs on modern stacks (e.g., vLLM on Ray).
• Manage vector databases to support AI/ML apps.
• Establish monitoring and observability pipelines for health and performance.
• Collaborate with cross-functional teams to improve infrastructure.
• Partner with product and architecture teams to define scalable solutions.

Skills

Python

AWS / Cloud

Transformer models

RL training

Monitoring & observability

Docker & Kubernetes

Ray / vLLM

CUDA

Education

Bachelor's degree in CS/Engineering

Master's degree in CS/Engineering

Tools

Docker

Kubernetes

Ray

vLLM

CUDA

Job description

JPMorgan Chase & Co. seeks a Senior MLOps Engineer (Executive Director) to collaborate with data scientists on building and deploying ML models on a modern MLOps stack. You will lead fine-tuning, RL, real-time and batch serving, and scale training on GPU clusters in AWS.

This role powers Personalization and Insights across Chase channels with high throughput, low latency experiences. You will work within a controlled environment with production validation, observability, and cross-functional