Senior MLOps Director for Personalization & Insights

Aumni; Palo Alto (CA); On-site; USD 180,000 - 260,000; Full time

Job summary

JPMorgan Chase in Palo Alto is seeking a Senior MLOps engineer to build and deploy ML models on a modern MLOps stack, collaborating with data scientists on the Recommendation Engine team.

You will implement tuning and reinforcement learning algorithms, run real-time and batch serving, and ensure monitoring, validation, and scalable infrastructure in AWS, helping power personalization across Chase’s consumer banking channels.

Qualifications

• BS in Computer Science or related field with 10+ years of experience or MS with 6+ years experience.
• Solid knowledge and extensive experience in Python or in cloud computing and AWS.
• Understanding of quantization techniques such as PTQ, AWQ etc. used to quantize LLMs for accelerating inference on specific GPU architectures
• Solid understanding of Transformer models and challenges involved in serving large transformer-based models
• Solid understanding of ML training, especially latest reinforcement learning algorithms such as GRPO and DAPO
• Experience in systems engineering fundamentals: caching, CUDA, autoscaling, high throughput, low latency
• Deep knowledge and passion for data science fundamentals, training and deploying models
• Experience in monitoring and observability tools to monitor model input/output and features stats

Responsibilities

• Build, deploy, and maintain robust pipelines for distributed training on GPU-enabled clusters to support scalable machine learning workflows.
• Develop and manage high-volume real-time and batch inference systems, ensuring optimal performance and reliability.
• Implement quantization techniques and deploy open-weight large language models (LLMs) on modern serving stacks such as vLLM on Ray 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 programming

ML model deployment

Distributed training

Monitoring & observability

CUDA

AWS / cloud computing

Education

BS in Computer Science or related field

MS in Computer Science or related field

Tools

Ray

vLLM

Docker

Kubernetes

AWS

Job description

JPMorgan Chase in Palo Alto is seeking a Senior MLOps engineer to build and deploy ML models on a modern MLOps stack, collaborating with data scientists on the Recommendation Engine team.

You will implement tuning and reinforcement learning algorithms, run real-time and batch serving, and ensure monitoring, validation, and scalable infrastructure in AWS, helping power personalization across Chase’s consumer banking channels.