Support projects as a security-conscious DevSecOps Engineer to support the deployment of artificial intelligence (AI) / machine learning (ML) models
Requirements
- Solid understanding of DevSecOps principles, including integrating security into CI/CD workflows for AI/ML models.
- Experience with Docker for containerizing ML models and managing container lifecycles, including building Docker images.
- Experience with Jenkins for building and automating CI/CD pipelines.
- Experience utilizing GitHub for version control, branching strategies, and CI/CD pipeline integration.
- Experience in OpenShift for deploying and managing containerized applications in a Kubernetes-based environment.
- Knowledge of cloud platforms (AWS, Azure, GCP) and hybrid cloud deployments, including cloud security (e.g., Managed Identities)
- Knowledge of Infrastructure as Code.
Responsibilities
- Design, implement, and maintain CI/CD pipelines for ML model deployment using Jenkins and GitHub.
- Containerize ML models using Docker and manage deployments on OpenShift.
- Integrate security checks and compliance validation into CI/CD workflows.
- Collaborate with data scientists and ML engineers to understand model requirements and deployment constraints.
- Automate infrastructure provisioning using tools like Terraform or Ansible.
- Monitor and troubleshoot deployment environments to ensure reliability and performance.
- Implement logging, monitoring, and alerting solutions for deployed ML services.
Other
- An ACTIVE and MAINTAINED 'TOP SECRET' Federal or DoD security clearance
- Bachelor's degree is required
- Minimum FOUR (4) years of relevant professional experience in AI/ML, DevSecOps, data engineering, or software development/engineering.
- Strong communication skills, with the ability to discuss technical topics with technical leaders and business topics with non-technical leaders.
- Ability to think strategically and drive innovation.
