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.

As a Site Reliability Engineer III at JPMorgan Chase within the Digital Private Markets /Aumni (A JP Morgan Chase Company), you will solve complex and broad business problems with simple and straightforward solutions. Through code and cloud infrastructure, you will configure, maintain, monitor, and optimize applications and their associated infrastructure to independently decompose and iteratively improve on existing solutions. You are a significant contributor to your team by sharing your knowledge of end-to-end operations, availability, reliability, and scalability of your application or platform. As MLops Engineer, you will solve complex and broad business problems with simple and straightforward solutions. Through code and cloud infrastructure, you will configure, maintain, monitor, and optimize the models produced by our data science teams and their associated. 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

• Guides and assists others in the areas of designing and deploying new AI/ML models in the cloud, gaining consensus from peers where appropriate
• Designs and implements automated continuous integration and continuous delivery pipelines for the Data Science teams to develop and train AI/ML models
• Writes and deploys infrastructure as code for the models and pipelines you support
• 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 site reliability engineering concepts and 3 years applied experience
• 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
• Ability to identify new technologies and relevant solutions to ensure design constraints are met by the Data Science and Machine Learning teams

• Experience with 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