About the Company
Abnormal Security is a leading AI-native human behavior security platform that leverages machine learning to stop sophisticated inbound attacks and detect compromised accounts across email and connected applications. Since its founding, the company has focused on enhancing security and enabling innovation through advanced AI-driven solutions. With a presence across the United States and Canada, Abnormal Security continues to support enterprises worldwide in protecting their digital environments.
About the Role
Abnormal Security is looking for an experienced Infrastructure/DevOps Engineer to strengthen its IT team. This role focuses on building and maintaining reliable, scalable, and secure infrastructure that supports advanced AI and machine learning platforms. The position involves close collaboration with IT, security, and engineering teams to enable fast experimentation, seamless deployments, and efficient operations. This is a fully remote position open to candidates based in the United States or Canada.
Responsibilities
- Design, build, and manage infrastructure supporting AI/ML pipelines, tools, and data platforms.
- Implement and maintain containerization (Docker) and orchestration (Kubernetes).
- Develop and optimize CI/CD pipelines integrated with ML workflows.
- Collaborate with security and compliance teams to meet data protection requirements.
- Automate infrastructure provisioning with Terraform, Ansible, or Pulumi.
- Monitor, log, and troubleshoot systems using Prometheus, Grafana, and ELK stack.
- Partner with AI and software engineers to improve platform scalability and performance.
- Create and maintain clear documentation for infrastructure processes.
Required Skills
- 4+ years of experience in DevOps, SRE, or Infrastructure Engineering.
- Strong expertise with AWS, Docker, and Kubernetes.
- Hands-on experience with infrastructure as code (Terraform, Ansible, or Pulumi).
- Proficiency in scripting languages such as Python or Bash.
- Background in CI/CD tools (GitHub Actions, Jenkins, CircleCI).
- Solid understanding of networking, security, and identity management in cloud environments.
- Experience supporting ML workloads and GPU infrastructure.
- Strong troubleshooting and collaboration skills.
Preferred Qualifications
- Familiarity with MLOps platforms (MLflow, Kubeflow, or SageMaker).
- Experience with model serving, feature stores, and AI platform infrastructure.
- Knowledge of logging and monitoring frameworks (Fluentd, Loki).
- Experience with data platforms (Snowflake, Databricks, Hadoop).
- AWS certification.
- Previous experience in high-growth startups or tech organizations.