Executive Director, ML & MLOps Engineering

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

Job summary

JPMorganChase in Palo Alto seeks a Senior MLOps Engineer to collaborate with data scientists, building and deploying ML models on a modern MLOps stack within our Personalization and Insights initiatives.

You will implement fine-tuning, RL, real-time and batch serving, model monitoring, and scalable infrastructure on AWS, focusing on low latency and high throughput with robust observability.

Qualifications

• BS in Computer Science or related Engineering with 10+ years of experience or MS with 6+ years.
• Strong knowledge of Python and cloud/AWS.
• Experience quantizing LLMs (PTQ/AWQ).
• Understanding of transformer models and large-scale serving.
• Experience with RL training methods (GRPO/DAPO).
• Systems engineering fundamentals: caching, CUDA, 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 scalable ML workloads.
• Implement quantization and deploy LLMs on modern serving stacks.
• Oversee vector databases for AI/ML applications.
• Establish comprehensive monitoring and observability pipelines.
• Collaborate with cross-functional teams to improve infrastructure.
• Partner with product/architecture to define scalable solutions.

Skills

Python

AWS

ML training

Transformer models

Reinforcement learning

Distributed training

Monitoring/Observability

Containers (Docker/Kubernetes)

Education

BS in Computer Science or related Engineering

MS in Computer Science or related Engineering

Tools

Docker

Kubernetes

Ray

vLLM

Job description

JPMorganChase in Palo Alto seeks a Senior MLOps Engineer to collaborate with data scientists, building and deploying ML models on a modern MLOps stack within our Personalization and Insights initiatives.

You will implement fine-tuning, RL, real-time and batch serving, model monitoring, and scalable infrastructure on AWS, focusing on low latency and high throughput with robust observability.