Senior DevOps Engineer

Company Overview:



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.

About the Role

We are building a rapidly scaling GenAI-powered SaaS platform that enables investigators to interact with complex case data through a conversational AI interface. Our system leverages RAG architecture and agentic GenAI workflows to deliver advanced AI capabilities in production.

We are looking for a Senior DevOps / Cloud Engineer to own our application services, cloud infrastructure, deployment pipelines, and production reliability in this dynamic AI environment.

This is a hands-on role focused on serverless architecture, LLM-based systems, and agentic workflows, working closely with Engineering and Customer Success to ensure the platform is reliable, scalable, and cost-efficient.

Key Responsibilities



• Own and manage application services running on GCP infrastructure, including serverless and managed services
• Design and maintain robust CI/CD pipelines for rapid, safe deployments
• Operate and optimize GenAI/LLM workloads in production, including RAG pipelines and agentic workflows
• Monitor and improve latency, cost, and reliability of AI-driven systems
• Troubleshoot complex production issues across application, data, and infrastructure layers
• Work with and optimize BigQuery-based data workflows, queries, and performance
• Support and debug multi-step AI pipelines and agent orchestration flows
• Implement and maintain observability (logging, metrics, tracing, alerting), including for AI pipelines
• Collaborate with engineering teams on architecture improvements for evolving GenAI systems
• Partner with Customer Success to investigate and resolve customer-impacting issues (minimal direct customer interaction)
• Enforce security and best practices in a sensitive data environment

What We’re Looking For



• A senior engineer who can own production systems end-to-end
• Strong problem-solver with the ability to debug complex, non-deterministic AI systems
• Comfortable working in a rapidly evolving GenAI and agentic architecture
• Pragmatic mindset — balancing performance, cost, and reliability
• High ownership and ability to work independently

Why Join Us



• Build and scale a real-world GenAI product with meaningful impact
• Work on cutting-edge challenges involving LLMs, RAG, and agentic systems
• Be part of a small, fast-moving, high-impact innovation team

• 5+ years of experience in DevOps / SRE / Cloud Engineering
• Strong hands-on experience with Google Cloud Platform (GCP)
• Proven experience with serverless architectures (Cloud Run, Cloud Functions, or similar)
• Experience working with BigQuery (querying, performance tuning, troubleshooting)
• Experience running and supporting production SaaS applications
• Hands-on experience with GenAI / LLM-based applications in production
• (including RAG systems, model APIs, or similar)
• Experience supporting or operating multi-step AI pipelines or agentic workflows
• Strong experience with CI/CD pipelines (GitHub Actions, etc.)
• Solid scripting/programming skills (Python, TypeScript, Bash, or similar)
• Experience with observability and monitoring tools

Preferred Qualifications



• Experience optimizing LLM performance, cost, and reliability at scale
• Familiarity with vector databases, embeddings, and retrieval systems
• Experience with infrastructure as code (Terraform or similar)
• Background in secure or regulated environments
• Experience in fast-scaling or experimental product environments