MLOps and Engineering

The AI, Data & Research unit at CyberArk is seeking a passionate MLOps and Engineering professional to build data-driven, ML-powered, and intelligent security solutions. This role involves designing, building, and maintaining a multi-tenant PaaS for ML pipelines and inference, ensuring scalability, reliability, and security. Responsibilities include infrastructure-as-code, Docker-based services, AWS solutions, CI/CD pipeline enhancement, security, observability, and collaboration with various teams.

Must Have

• Design, build, and maintain infrastructure-as-code using Python and AWS services for deployment.
• Architect, build, and manage Docker-based services.
• Lead the design and implementation of solutions using AWS services such as SageMaker, Lambda, Step Functions, SageMaker Pipelines, Batch Transform, and Real-Time Endpoints.
• Enhance and maintain CI/CD pipelines (Jenkins and shared libraries).
• Ensure multi-tenant security and tenant isolation across the platform.
• Define and implement observability and monitoring practices with Datadog and other tools.
• Collaborate closely with Data Scientists, Data engineers, MLEs, Product Managers, and other engineering teams to integrate ML workflows.
• Mentor junior engineers and promote engineering best practices.
• Bachelor’s degree in computer science, Software Engineering, or a related field.
• 4+ years of hands-on development experience with Python and AWS.
• Proven experience with infrastructure as code (preferably AWS CDK, Terraform, or CloudFormation).
• Strong knowledge of AWS architecture and services, particularly in data/ML workloads.
• Deep experience with CI/CD pipelines (Jenkins or similar).
• Strong expertise in Docker and containerized applications.
• Demonstrated knowledge of cloud security, scalability, and tenant isolation.
• Hands-on experience with observability platforms (preferably Datadog).

Good to Have

• Background in MLOps, Data Platforms, or Machine Learning workflows.
• Experience with additional monitoring and logging tools (CloudWatch, Prometheus, ELK).
• Leadership experience in scaling cloud-native platforms.
• Experience in information security.
• Understanding of identity & access management, secrets management, or zero-trust architecture.

##### Job Description

The AI, Data & Research unit is at the forefront of CyberArk’s innovation, building data-driven, ML-powered, and intelligent security solutions. We are looking for a passionate to join our team of seasoned ML engineers.

You will play a critical role in building a multi-tenant PaaS for ML pipelines and inference, ensuring scalability, reliability, and security. You will take ownership of critical platform components, drive best practices, and mentor other engineers.

• Design, build, and maintain infrastructure-as-code using Python and AWS services for deployment.

• Architect, build, and manage Docker-based services.

• Lead the design and implementation of solutions using AWS services such as SageMaker, Lambda, Step Functions, SageMaker Pipelines, Batch Transform, and Real-Time Endpoints.

• Enhance and maintain CI/CD pipelines (Jenkins and shared libraries).

• Ensure multi-tenant security and tenant isolation across the platform.

• Define and implement observability and monitoring practices with Datadog and other tools.

• Collaborate closely with Data Scientists, Data engineers, MLEs, Product Managers, and other engineering teams to integrate ML workflows.

• Mentor junior engineers and promote engineering best practices.

#LI-Hybrid

#LI-OS1

##### Qualifications

• Bachelor’s degree in computer science, Software Engineering, or a related field.

• 4+ years of hands-on development experience with Python and AWS.

• Proven experience with infrastructure as code (preferably AWS CDK, Terraform, or CloudFormation).

• Strong knowledge of AWS architecture and services, particularly in data/ML workloads.

• Deep experience with CI/CD pipelines (Jenkins or similar).

• Strong expertise in Docker and containerized applications.

• Demonstrated knowledge of cloud security, scalability, and tenant isolation.

• Hands-on experience with observability platforms (preferably Datadog).

• Self-motivated and goal-oriented with a high work ethic.

##### Additional Information

• Background in MLOps, Data Platforms, or Machine Learning workflows.

• Experience with additional monitoring and logging tools (CloudWatch, Prometheus, ELK).

• Leadership experience in scaling cloud-native platforms.

• Experience in information security – an advantage

• Understanding of identity & access management, secrets management, or zero-trust architecture - Bonus.