DevOps Engineer designing and operating AWS infrastructure within industrial IoT environments. Working on systems that ensure security, resilience, and end - to - end observability.
About the role
- Cloud Platform Engineer (ML DevOps) developing and managing CI/CD pipelines for ML workflows in a leading insurance company. Collaborating with data scientists and ensuring infrastructure security and compliance.
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.
Requirements
- 4+ years of experience with software development languages such as Python, Java.
- 4+ years of experience with Cloud Technologies such as Azure and AWS.
- 4+ years of experience with DevOps.
- 4+ years of experienced with Infrastructure as Code technologies such as Terraform, Ansible, Chef or Puppet.
- Exposure to machine learning frameworks and distributed data processing tools like Apache Spark or equivalents.
Benefits
- Health insurance
- 401(k) matching
- Flexible work hours
- Paid time off
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