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Chase · 13 hours ago

Lead Machine Learning Engineer-MLOps

450 W 33rd St, New York, NY, 10001, US

Full-time

Onsite

Senior Level, Lead/Staff

$164K/yr - $260K/yr

4+ years exp

Chase is a leading financial institution offering innovative financial solutions. They are seeking a Senior MLOps engineer to work closely with Data Scientists to build and deploy ML models on a modern MLOps stack.

BankingFinancial Services

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

Qualification

PythonAWSMLOpsBig Data ToolsMonitoring ToolsContainerizationAction OrientedAnalytical MindsetCollaboration

Required

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

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