MLOps Infrastructure Engineer - AI Security

MLOps Infrastructure Engineer - AI Security

About the Role :

We are looking for an experienced MLOps Infrastructure Engineer - AI Security to design, build, and maintain scalable infrastructure and ML pipelines with integrated AI security controls.

The ideal candidate will have strong experience in MLOps platforms, ML workflow automation, cloud infrastructure, containerization, orchestration, and scripting/programming, along with an understanding of integrating security checks into AI/ML development and deployment pipelines.

You will work closely with AI/ML engineers, security researchers, DevOps teams, and platform engineers to build secure, reliable, and scalable ML infrastructure.

Key Responsibilities :

- Design, build, maintain, and optimize MLOps infrastructure and ML pipelines.

- Integrate AI/ML security checks and controls into CI/CD and ML workflows.

- Build automated pipelines for model development, validation, testing, deployment, and monitoring.

- Implement security gates and automated checks across the ML lifecycle.

- Manage and optimize MLOps platforms and workflow orchestration tools.

- Build and manage containerized ML workloads using Docker and orchestration platforms such as Kubernetes.

- Design and manage scalable cloud infrastructure for AI/ML workloads.

- Implement infrastructure automation, monitoring, logging, and observability for ML platforms.

- Collaborate with AI security teams to operationalize security research and integrate security controls into production ML pipelines.

- Troubleshoot infrastructure, pipeline, deployment, and performance issues.

- Develop automation scripts and tools to improve the reliability and security of ML workflows.

- Establish best practices for secure MLOps, infrastructure-as-code, CI/CD, and ML model deployment.

Required Skills & Experience :

- Strong experience in MLOps, ML infrastructure, or DevOps for AI/ML workloads.

- Hands-on experience with MLOps platforms and ML workflow/orchestration tools.

- Strong experience with containerization and orchestration, particularly Docker and Kubernetes.

- Experience managing cloud infrastructure for ML/AI workloads on platforms such as AWS, Azure, or GCP.

- Experience designing and implementing CI/CD pipelines for ML applications.

- Strong scripting and programming skills in Python, Bash, or similar languages.

- Experience with infrastructure automation and Infrastructure as Code (IaC) tools such as Terraform.

- Understanding of ML lifecycle management, model deployment, monitoring, and versioning.

- Experience integrating security, compliance, and validation checks into automated pipelines.

- Strong troubleshooting, debugging, and problem-solving skills.

Good to Have :

- Experience with AI/ML security or secure MLOps practices.

- Knowledge of LLM/GenAI deployment and security.

- Familiarity with model scanning, vulnerability assessment, data validation, and security testing.

- Experience with tools such as MLflow, Kubeflow, Airflow, Argo Workflows, or similar platforms.

- Knowledge of cloud-native security and Kubernetes security.

- Understanding of DevSecOps principles and secure CI/CD practices.

- Experience with observability tools, logging, monitoring, and alerting frameworks.

Education :

- Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related field.