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Job Description
About the role
About Cellebrite:
Cellebrite’s (Nasdaq: CLBT) mission is to enable its global customers to protect and save lives by enhancing digital investigations and intelligence gathering to accelerate justice in communities around the world. Cellebrite’s AI-powered Digital Investigation Platform enables customers to lawfully access, collect, analyze and share digital evidence in legally sanctioned investigations while preserving data privacy. Thousands of public safety organizations, intelligence agencies and businesses rely on Cellebrite’s digital forensic and investigative solutions—available via cloud, on-premises and hybrid deployments—to close cases faster and safeguard communities.
To learn more, visit us at www.cellebrite.com, https://investors.cellebrite.com/investors and find us on social media @Cellebrite.
What is your mission?
As a Senior DevOps Engineer, you'll take charge of automating how we deploy infrastructure and applications with a strong focus on the infrastructure powering our AI/ML systems.
You'll also lead efforts to make our processes better, helping engineering and product teams work faster and smarter, without compromising service quality.
You bring an AI-first mindset to your own daily work using AI tools to move faster, automate more, and continuously raise the bar on how the team operates. Your work supports Cellebrite's mission to protect lives, accelerate justice, and ensure data privacy.
What you'll do
Design, develop, and maintain automation processes to deploy large-scale, enterprise-grade applications, including AI/ML workloads and pipelines.
Serve as the go-to expert for CI/CD processes supporting Cellebrite's cloud and AI products.
Architect, deploy, and operate infrastructure for AI systems, including Amazon Bedrock, model endpoints and inference pipelines, following AI infrastructure best practices.
Actively use AI tools and agents (e.g., coding assistants, AI-driven automation, LLM-based troubleshooting) in day-to-day DevOps work to increase velocity, reduce toil, and improve quality and champion this way of working across the team.
Stay current with the fast-moving global AI landscape (new models, frameworks, cloud AI services, industry best practices) and bring relevant advances back into the team's tooling and processes.
Partner with R&D, Product Management, and Architecture to deliver key operational metrics and feedback that support high product availability.
Investigate reported and suspected outages and other issues, including those affecting AI/ML services.
Oversee documentation of issues and resolutions; maintain a knowledge base of known issues.
Produce, oversee production of, and maintain documentation such as architecture diagrams, system designs, and runbooks.
Work alongside fellow SREs, DevOps Engineers, and developers to solve problems and investigate anomalies.
Participate in an on-call rotation to help maintain our 99.95% uptime SLO.
Requirements
7+ years of experience working with AWS, with a proven track record of building complex infrastructure.
2+ years of hands-on experience working with AI/ML systems in production, applying best practices for reliability, cost, security, and scalability of AI infrastructure.
Strong, hands-on experience with Amazon Bedrock and the broader AWS AI/ML stack (e.g., SageMaker, vector databases).
Demonstrated habit of using AI tools in daily engineering work (e.g., AI coding assistants, agentic workflows, LLM-powered automation) not just knowledge of AI infrastructure, but active personal use to work smarter and faster.
A continuous-learning mindset toward AI genuinely curious about the field, keeps skills current as technology evolves, and enjoys exploring new AI capabilities and applying them practically.
Working knowledge of Google Cloud Platform (GCP), with the ability to operate and troubleshoot across a multi-cloud environment.
Strong experience with networking, containerization (Kubernetes), and Linux system administration.
Deep understanding of Infrastructure as Code (Terraform, CloudFormation, etc.).
Strong experience with Kubernetes, preferably with AWS EKS.
Experience scaling and maintaining high-availability production systems.
Experience with DevOps release management, versioning, build management, automation scripting, and CI tools such as GitHub Actions.
Excellent verbal and written communication skills in English.
Ability to serve as a guide to the team and provide best practices as a DevOps Tech Lead, particularly around AI infrastructure and AI-augmented workflows.