Lead Machine Learning Engineer-MLOps

Fairygodboss; New York (NY); On-site; USD 180,000 - 260,000; Full time

Benefits offered by this job

Health care coverage

On-site health centers

Retirement savings plan

Tuition reimbursement

Mental health support

Job summary

JPMorgan Chase & Co. seeks a senior MLOps engineer to collaborate with data scientists, building and deploying ML models on a modern MLOps stack. Lead on the Recommendation Engine team, crafting pipelines for distributed training and real-time inference on GPU clusters in AWS.

You will quantize models, manage vector databases, and maintain observability pipelines while working with product and architecture to deliver scalable solutions.

Qualifications

• BS in CS or related Engineering with 6+ years experience or MS with 4+ years experience.
• Strong Python and cloud computing experience, preferably AWS.
• Experience with quantization techniques (PTQ, AWQ) for LLMs to accelerate inference.
• Solid systems engineering fundamentals: caching, CUDA, autoscaling, high throughput, low latency.

Responsibilities

• Build, deploy, and maintain robust pipelines for distributed training on GPU-enabled clusters.
• Develop and manage pipelines for high-throughput real-time and batch inference.
• Implement quantization techniques and deploy large language models for efficient inference.
• Oversee vector databases to support advanced AI/ML applications.
• Establish and maintain monitoring and observability pipelines for system health and performance.
• Collaborate with cross-functional teams to integrate new technologies and improve infrastructure.
• Partner with product, architecture, and engineering teams to define scalable solutions.

Skills

Python

AWS

LLM quantization

CUDA

Distributed training

Monitoring & observability

Containers/Docker

Kubernetes

Airflow/Kubeflow

Ray/Spark

Education

BS in Computer Science or related Engineering (6+ years)

MS in Computer Science or related Engineering (4+ years)

Tools

Ray

DuckDB

Spark

Docker

Kubernetes

ECS

Airflow

Kubeflow

vllm/SGLang

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 and in cloud computing, preferably AWS
• Understanding of quantization techniques such as PTQ, AWQ etc. used to quantize LLMs for accelerating inference on specific GPU architectures
• Experience in systems engineering fundamentals: caching, CUDA, autoscaling, high throughput, low latency, x-region resilient applications
• 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 and in training/inference systems such as Ray, vllm/SGLang

• Solid grounding in engineering fundamentals and analytical mindset

Preferred qualifications, capabilities, and skills

• Experience with recommendation and personalization systems is a plus.
• CUDA experience is a big plus

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

• Good knowledge of Databases

ABOUT US

JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world's most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.

We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.

We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.

JPMorgan Chase & Co. is an Equal Opportunity Employer, including Disability/Veterans

ABOUT THE TEAM

J.P. Morgan's Commercial & Investment Bank is a global leader across banking, markets, securities services and payments. Corporations, governments and institutions throughout the world entrust us with their business in more than 100 countries. The Commercial & Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world.