Lead ML & MLOps Engineer for Real-Time Personalization

J.P. Morgan; New York (NY); On-site; USD 130,000 - 170,000; Full time

Job summary

A leading global financial institution seeks a Senior MLOps Engineer to build and deploy machine learning models using a modern MLOps stack. The role involves creating and maintaining robust pipelines for distributed training and real-time inference, optimizing ML application performance, and collaborating with cross-functional teams. Candidates should possess a strong background in Python, cloud services (preferably AWS), and big data tools with a degree in Computer Science or related field. Experience in recommendation systems is a plus.

Qualifications

• 6+ years of experience with a BS or 4+ years with an MS in a relevant field.
• Extensive experience in Python.
• Familiarity with AWS cloud services.
• Deep knowledge of model training and deployment.

Responsibilities

• Build and maintain pipelines for distributed training on GPU clusters.
• Manage real-time and batch inference pipelines.
• Oversee vector databases for AI applications.
• Establish monitoring and observability pipelines.

Skills

Python

Cloud computing (AWS)

Data science fundamentals

Monitoring and observability tools

Big data/ML tools (Ray, DuckDB, Spark)

Analytical mindset

Iterative development

Education

BS in Computer Science or related Engineering field

MS in Computer Science or related Engineering field

Tools

Docker

Kubernetes

Airflow

Kubeflow

Job description

A leading global financial institution seeks a Senior MLOps Engineer to build and deploy machine learning models using a modern MLOps stack. The role involves creating and maintaining robust pipelines for distributed training and real-time inference, optimizing ML application performance, and collaborating with cross-functional teams. Candidates should possess a strong background in Python, cloud services (preferably AWS), and big data tools with a degree in Computer Science or related field. Experience in recommendation systems is a plus.