Senior MLOps Engineer for Recommender Systems

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

Job summary

JPMorgan Chase & Co. is seeking a Senior MLOps engineer to partner with Data Scientists to build and deploy ML models on a modern MLOps stack in a Palo Alto setting.

You will lead pipelines for distributed training on GPU clusters, model serving at scale, and continuous validation in a controlled environment. The role focuses on production ML workflows within Personalization and Insights, deploying on AWS, and enabling high-throughput, low-latency applications that power personalized experiences

Qualifications

• BS in Computer Science or related field with 6+ years experience, or MS with 4+ years.
• Strong Python and AWS cloud experience.
• Knowledge of quantization techniques for LLMs.
• Experience with caching, CUDA, autoscaling, high throughput, low latency.
• Data science fundamentals and model training/deployment.
• Experience with monitoring/observability for ML models.
• Familiarity with Ray, DuckDB, Spark and training/inference systems.

Responsibilities

• Build, deploy, and maintain pipelines for distributed training on GPU-enabled clusters.
• Develop and manage real-time and batch inference pipelines.
• Execute quantization and deploy LLMs for efficient inference.
• Optimize vector DBs to support AI apps.
• Establish comprehensive monitoring and observability pipelines.
• Collaborate with cross-functional teams to adopt new tech.
• Partner with product and architecture teams to design scalable solutions.

Skills

Python

AWS Cloud

Quantization PTQ/AWQ

CUDA

Distributed training

Monitoring/Observability

Ray/DuckDB/Spark

vLLM

Education

BS in CS or related field

MS in CS or related field

Tools

Ray

DuckDB

Spark

vllm

Job description

JPMorgan Chase & Co. is seeking a Senior MLOps engineer to partner with Data Scientists to build and deploy ML models on a modern MLOps stack in a Palo Alto setting.

You will lead pipelines for distributed training on GPU clusters, model serving at scale, and continuous validation in a controlled environment. The role focuses on production ML workflows within Personalization and Insights, deploying on AWS, and enabling high-throughput, low-latency applications that power personalized experiences