Description There’s nothing more exciting than being at the center of a rapidly growing field in technology and applying your skillsets to drive innovation and modernize the world's most complex and mission-critical systems. The Aumni team at JPMorgan Chase is looking for an MLOps engineer to help us build out our core model hosting, deployment, and monitoring infrastructure in AWS. An MLOps Engineer III within the Digital Private Markets department will help us solve complex and broad business problems with practical and straightforward solutions. Through code and cloud infrastructure, you will configure, maintain, and monitor the systems and models produced by our data science teams. You are a significant contributor to your team by sharing your knowledge of end-to-end operations, availability, reliability, and scalability in the AI/ML space. Job responsibilities Writes and deploys infrastructure as code for the models and pipelines you support Designs and implements automated continuous integration and continuous delivery pipelines for the Data Science teams to develop and train AI/ML models Assists Data Scientists when deploying AI/ML models in the cloud Collaborates with technical experts, key stakeholders, and team members to resolve complex technical problems Understands the importance of monitoring and observability in the AI/ML space – i.e. service level indicators and utilizes service level objectives Proactively resolve issues before they impact internal and external stakeholders of deployed models Supports the adoption of MLOps best practices within your team Required qualifications, capabilities, and skills Formal training or certification on MLOps concepts Understanding of MLOps culture and principles and familiarity with how to implement associated concepts at scale Domain knowledge of machine learning applications and technical processes within the AWS ecosystem Experience with infrastructure as code tooling such as Terraform, Cloudformation Experience with container and container orchestration such as ECS, Kubernetes, and Docker Knowledge of continuous integration and continuous delivery tools like Jenkins, GitLab, or Github Actions Proficiency in the following programming languages: Python, Bash Hands-on knowledge of Linux and networking internals Understanding of the different roles served by data engineers, data scientists, machine learning engineers, and system architects, and how MLOps contributes to each of these workstreams Preferred qualifications, capabilities, and skills Experience with ML model training and deployment pipelines, managing scoring endpoints Familiarity with observability concepts and telemetry collection using tools such as Datadog, Grafana, Prometheus, Splunk, and others Understanding of data engineering platforms such as Databricks or Snowflake, and machine learning platforms such as AWS Sagemaker Comfortable troubleshooting common containerization technologies and issues Ability to proactively recognize road blocks and demonstrates interest in learning technology that facilitates innovation Ability to identify new technologies and relevant solutions to improve design patterns where appropriate Comfortable with team collaboration, presenting technical concepts to non-technical audiences, researching the pros and cons of various system design options