MLOps Engineer

Description

Come join the company building the security operating model for the age of AI. AI has changed how data is used — and security must change with it. Cyera's mission is to empower businesses to accelerate AI adoption by defining a holistic approach to securing AI - from data to access to model. Backed by the world's leading investors and working with a large and growing list of Fortune 1000 companies, we are looking for world-class talent to join us as we usher in the new era of data and AI security.

We are looking for a skilled DevOps Engineer with a passion for ML & AI workloads to build and operate the infrastructure behind our models, LLM features and research. You will work on high-scale systems that process petabytes of data, with best-in-class technologies, alongside ML Engineers, AI Engineers, the Research group and the company's DevOps team.

What You'll Do:

ML & LLM Infrastructure

• Design, deploy and maintain the infrastructure for model serving, LLM gateways and LLM observability, across clouds, deployment types (SaaS, customer-hosted and government clouds) and new regions.
• Own GPU infrastructure, including its capacity and cost efficiency.
• Build secure, private connectivity to LLM providers and between clouds.
• Keep our systems reliable: monitor them proactively, find and resolve issues before they affect customers, and take part in on-call.
• Automate everything, and keep up with the latest infrastructure tools: evaluate new frameworks and take the ones that prove themselves from POC to production.

Research Infrastructure & Collaboration

• Provide the infrastructure for the Research group, including GPU clusters for training, experimentation and batch workloads.
• Support the infrastructure for all ML and AI features and workloads, for ML and AI engineers and other R&D groups.
• Keep ML and research infrastructure aligned with the company's DevOps direction and Security requirements.

Requirements

• 4+ years of experience in DevOps Engineering or Site Reliability.
• Strong experience running production infrastructure on AWS, GCP or Azure.
• Hands-on experience with Kubernetes and Docker.
• Experience with Infrastructure as Code (e.g. Terraform) and CI/CD pipelines.
• Solid networking fundamentals (load balancing, private connectivity, protocols).
• Experience building and supporting distributed, high-scale production environments, including monitoring and logging.
• Linux and scripting skills.
• A passion for ML & AI workloads, and the drive to grow in this space.
• Strong problem-solving and communication skills, and the ability to work across R&D teams.

Advantages:

• Experience running ML models or LLM-based systems in production.
• Experience with GPU workloads on Kubernetes, GPU sharing, capacity planning and cost optimization.
• Experience with LLM serving, LLM gateways or inference-aware load balancing.
• Experience deploying ML and research tooling (e.g. experiment tracking, LLM observability).
• Experience with distributed compute frameworks for large-scale inference.
• Knowledge of vector databases, embeddings or retrieval pipelines.
• Experience with customer-hosted or government cloud deployments.
• Familiarity with SOC2 / ISO 27001 requirements.

Why Join Us?

At Cyera, we care about collaboration, innovation, and agility. We take “teamwork” seriously—with our inclusive and supportive culture at the forefront—and we’re just as serious about nurturing Cyerans to grow, both personally and professionally.

Feel free to apply even if your experience doesn’t tick every box.

We’re building something special here—and we welcome Cyerans with diverse backgrounds, perspectives, and experiences.