Lead ML Engineer - MLOps

Salary: $61,000 - 101,000 per year Requirements:

• We require a BS in Computer Science or a related engineering discipline with 6+ years of experience, or an MS in Computer Science or a related engineering discipline with 4+ years of experience.
• We need strong, hands-on experience with Python.
• We expect solid cloud computing fundamentals, ideally with AWS.
• We value deep knowledge of data science fundamentals, including model training and deployment.
• We need experience with monitoring and observability tools for tracking model inputs, outputs, and feature statistics.
• We look for operational experience with big data and machine learning tools such as Ray, DuckDB, and Spark.
• We want strong engineering fundamentals and an analytical mindset.
• We expect an action-oriented approach and iterative development habits.
• Experience with recommendation and personalization systems is preferred.
• We value experience with containers, container orchestration platforms such as Kubernetes and ECS, and DAG orchestration tools such as Airflow and Kubeflow.
• Good knowledge of databases is preferred.

• We build, deploy, and maintain resilient pipelines for distributed training on GPU-enabled clusters to support scalable machine learning workflows.
• We develop and manage pipelines for both high-throughput real-time inference and batch inference, with a focus on performance and reliability.
• We implement quantization techniques and deploy large language models to improve efficiency and resource usage.
• We oversee vector database management and optimization to support advanced AI and machine learning use cases.
• We establish and maintain monitoring and observability pipelines to track system health, performance, and rapid issue resolution.
• We collaborate with cross-functional teams to introduce new technologies and continuously enhance our infrastructure.
• We partner with product, architecture, and other engineering teams to define scalable, high-performing technical solutions.

• AI
• Airflow
• AWS
• Big Data
• Cloud
• Support
• Kubeflow
• Kubernetes
• Machine Learning
• Model Training
• Python
• Spark
• Docker
• MLOps
• Model Serving

More:

We are collaborating with hackajob to connect with exceptional professionals for this opportunity. We are building the Personalization and Insights product, which supports high-throughput, low-latency applications powered by state-of-the-art machine learning architectures deployed on AWS. Our work enables personalized experiences across Chase Consumer & Community Banking channels, combining traditional banking with experiences in travel, merchant offer shopping, and dining. We offer a competitive total rewards package, eligibility-based incentives, and a broad range of benefits and programs, including health coverage, wellness centers, retirement savings, backup childcare, tuition reimbursement, mental health support, and financial coaching. We are committed to diversity, inclusion, and equal opportunity, and we provide reasonable accommodations where needed. We are part of J.P. Morgans Commercial & Investment Bank, a global leader in banking, markets, securities services, and payments, serving corporations, governments, and institutions in more than 100 countries.

last updated 37 week of 2026