Cloud Platform Engineer (ML DevOps)

Allstate•McCullom Lake, IL

1d•$90,700 - $135,000

About The Position

At Allstate, great things happen when our people work together to protect families and their belongings from life’s uncertainties. And for more than 90 years, our innovative drive has kept us a step ahead of our customers’ evolving needs. From advocating for seat belts, air bags and graduated driving laws, to being an industry leader in pricing sophistication, telematics, and, more recently, device and identity protection. Job Description Software Engineer Senior Consultant II implements applications following 12-factor principles to build out the product and iterative enhancements. They own the full stack of software products, developing and implementing frontends (web or mobile), and backend services. They leverage test driven development and continuous integration to ensure agility and quality of products. They actively participate in the decision-making process of the team ensuring that the simplest appropriate technology and design is chosen to meet user needs.

Requirements

4+ years of experience with software development languages such as Python, Java. (Preferred).
4+ years of experience with Cloud Technologies such as Azure and AWS. (Preferred).
4+ years of experience with DevOps. (Preferred).
4+ years of experienced with Infrastructure as Code technologies such as Terraform, Ansible, Chef or Puppet. (Preferred).
Exposure to machine learning frameworks and distributed data processing tools like Apache Spark or equivalents.

Responsibilities

Designs, builds, and maintains infrastructure for ML experimentation, model training, and deployment.
Develops and manages CI/CD pipelines for ML workflows (data ingestion, model training, testing, and deployment).
Implements and manages ML platforms (e.g., Azure MLStudio, Fabric, MLflow, Kubeflow, SageMaker, Vertex AI) to support reproducibility and scalability.
Creates tools and environments to automate data versioning, model tracking, and artifact management.
Collaborates with data scientists to enable self-service access to compute resources and production systems.
Monitors, logs, and alerts on ML system health and model performance in production.
Enforces MLOps best practices across teams, including governance, model validation, and rollback strategies.
Ensures infrastructure security, cost-efficiency, and compliance.
Practices daily paired programming and test-driven development in writing software and building product
Participates in executing the strategy, keeping the customer needs and wants in mind
Establishes continuous integration, continuous delivery, and continuous deployment pipelines and practices
Participates in retrospectives to gather feedback and derive actionable items to improve the team and the product
Participates in iteration planning meetings ensuring that the team has a common understanding of each story and chores in a team’s backlog