Senior MLOps Lead — Real-Time ML Pipelines on AWS

Testing Site - JPMC; New York (NY); Vor Ort; USD 180.000 - 240.000; Vollzeit

Zusammenfassung

Testing Site - JPMC is seeking a Senior MLOps engineer to collaborate with Data Scientists to build and deploy ML models on a modern MLOps stack. You will lead distributed training on GPU clusters and develop scalable pipelines for real-time inference and batch processing.

You will deploy large language models with quantization, manage vector databases, and establish comprehensive monitoring and observability for a robust, production-grade ML platform in an AWS-based Personalization environment.

Qualifikationen

• BS or MS in computer science or related engineering field.
• BS with 6+ years of experience, or MS with 4+ years.
• Strong Python programming proficiency.
• Cloud experience, preferably AWS.
• Experience with monitoring and observability tools.
• Experience with Ray, DuckDB, Spark.

Aufgaben

• Build, deploy, and maintain pipelines for distributed training on GPU clusters.
• Develop real-time and batch inference pipelines for high throughput.
• Deploy large language models and quantization techniques.
• Manage vector databases for advanced AI applications.
• 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.

Kenntnisse

Python

AWS cloud

Data science basics

Monitoring & observability

Distributed training

Ausbildung

BS in Computer Science or related Engineering

Tools

Ray

DuckDB

Spark

Docker

Kubernetes

ECS

Airflow

Kubeflow

Jobbeschreibung

Testing Site - JPMC is seeking a Senior MLOps engineer to collaborate with Data Scientists to build and deploy ML models on a modern MLOps stack. You will lead distributed training on GPU clusters and develop scalable pipelines for real-time inference and batch processing.

You will deploy large language models with quantization, manage vector databases, and establish comprehensive monitoring and observability for a robust, production-grade ML platform in an AWS-based Personalization environment.