Machine Learning Engineer-MLOps

JPMorgan Chase & Co. • New York, NY

13h

About The Position

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.

Requirements

• BS in Computer Science or related Engineering field with 3+ years of experience Or MS degree in Computer Science or related Engineering field with 2+ 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

Nice To Haves

• 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

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.

Benefits

• 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level