Lead Machine Learning Engineer-MLOps

Testing Site - JPMC; New York (NY); On-site; USD 180,000 - 240,000; Full time

Job summary

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.

Qualifications

• 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.

Responsibilities

• 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.

Skills

Python

AWS cloud

Data science basics

Monitoring & observability

Distributed training

Education

BS in Computer Science or related Engineering

Tools

Ray

DuckDB

Spark

Docker

Kubernetes

ECS

Airflow

Kubeflow

Job description

We are looking for a Senior MLOps engineer to work closely with Data Scientists to build and deploy ML models on a modern MLOps stack.

As Lead Machine Learning Engineer on the Recommendation Engine team, you’ll build and maintain pipelines for distributed model training on large compute clusters, batch/real-time model serving, hyperparameter tuning at scale, model monitoring, production validation and other activities vital for model development, testing and deployment in a well-managed, controlled environment.

Our product, Personalization and Insights, builds and supports high throughput, low latency applications which leverage state of the art machine learning architectures, and which are deployed in AWS. These applications power personalized experiences across Chase Consumer & Community Banking channels, to help weave a user experience that includes traditional banking services with other services in the Travel, Merchant Offer Shopping, and Dining spaces.

Job responsibilities

• Build, deploy, and maintain robust pipelines for distributed training on GPU-enabled clusters to support scalable machine learning workflows.

• Develop and manage pipelines for high-throughput, real-time inference as well as batch inference, ensuring optimal performance and reliability.

• Implement quantization techniques and deploy large language models (LLMs) 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.

Required qualifications, capabilities, and skills

• BS in Computer Science or related Engineering field with 6+ years of experience Or MS degree in Computer Science or related Engineering field with 4+ years experience.

• Solid knowledge and extensive experience in Python

• Solid fundamentals in cloud computing, preferably AWS

• 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

• Operational experience in big data/ML tools such as Ray, DuckDB, Spark

• Solid grounding in engineering fundamentals and analytical mindset

• Action Oriented and iterative development

Preferred qualifications, capabilities, and skills

• Experience with recommendation and personalization systems is a plus.

• Solid fundamentals and experience in containers (docker ecosystem), container orchestration systems [Kubernetes, ECS], DAG orchestration [Airflow, Kubeflow etc]

• Good knowledge of Databases