Senior DevOps Engineer – ML & AI Platforms

Cyera is on a mission to protect one of the world's most valuable resources: data. Our AI-native platform gives organizations a complete view of where their data lives, how it’s used, and how to keep it safe, so they can reduce risk and unlock the full value of their data, wherever it is.

Since our founding in 2021, we’ve grown fast- Cyera-fast - securing over $1.3 billion in funding from the biggest pockets on the planet and establishing a global team. Today, Cyera is the fastest-growing data security company on the planet, trusted by the Fortune 500 and beyond.

As we expand our AI-powered capabilities, we are looking for an experienced Senior DevOps Engineer with expertise in ML & AI platforms to build, scale, and operate the ML infrastructure that powers our data intelligence engine. This is a high-impact role that sits at the intersection of ML, infrastructure, and security—responsible for enabling reliable, scalable, and efficient model development, training, deployment, and monitoring.

You will work closely with our Data, AI, DevOps, and Backend teams to architect next-generation ML systems that support large-scale processing, model lifecycle automation, and cloud-native inference across AWS, GCP, and Azure.

Build & Operate ML Infrastructure

• Design, implement, and maintain the infrastructure that enables reliable, scalable ML operations across the organization.
• Maintain core ML platform services such as model registries, artifact stores, monitoring, and observability, enabling experiment tracking and model versioning systems.

Enable Scalable Inference & Automation

• Architect CI/CD processes for ML models, including automated testing, benchmarking, validation, and deployment.
• Integrate ML workflows into our microservices-based platform and multi-cloud architecture.
• Optimize real-time inference performance, cost efficiency, and reliability.

Observability, Reliability & Governance

• Build monitoring, alerting, and logging solutions for model performance, drift, data quality, and system health.
• Work closely with security teams to ensure compliance, governance, and risk mitigation for ML assets.
• Troubleshoot and resolve production issues across distributed systems with petabyte-scale data.

Cross-Team Collaboration

• Partner with Data Scientists and ML Engineers to streamline research-to-production workflows.
• Work with DevOps and Backend teams on resource planning, automation, and cloud infrastructure optimization.
• Participate in architectural POCs and contribute to the evolution of our AI platform.