Senior DevOps Engineer (AI)
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
TL;DR
Senior DevOps Engineer (GCP/AI): Building and scaling a GenAI-powered SaaS platform for digital investigations with an accent on serverless architecture, RAG pipelines, and agentic workflows. Focus on optimizing LLM production workloads, ensuring system reliability, and managing BigQuery-based data workflows.
Location: Must be based in the USA (Remote)
Company
is a global leader in digital investigations, providing an AI-powered platform to protect and save lives by enhancing intelligence gathering.
What you will do
- Own and manage application services on GCP infrastructure, focusing on serverless and managed services.
- Design and maintain robust CI/CD pipelines for rapid and safe deployments.
- Operate and optimize production GenAI/LLM workloads, including RAG pipelines and agentic workflows.
- Monitor and improve latency, cost, and reliability of AI-driven systems.
- Optimize BigQuery-based data workflows, queries, and performance.
- Implement comprehensive observability including logging, metrics, tracing, and alerting for AI pipelines.
Requirements
- 5+ years of experience in DevOps, SRE, or Cloud Engineering.
- Strong hands-on experience with Google Cloud Platform (GCP).
- Proven experience with serverless architectures (Cloud Run, Cloud Functions).
- Experience running and supporting production SaaS applications and BigQuery.
- Hands-on experience with GenAI/LLM-based applications in production (RAG systems, model APIs).
- Solid scripting and programming skills in Python, TypeScript, or Bash.
Nice to have
- Experience optimizing LLM performance, cost, and reliability at scale.
- Familiarity with vector databases, embeddings, and retrieval systems.
- Experience with Infrastructure as Code (Terraform).
- Background in secure or regulated environments.
Culture & Benefits
- Opportunity to build and scale a real-world GenAI product with meaningful global impact.
- Work on cutting-edge challenges involving LLMs, RAG, and agentic systems.
- Collaboration within a small, fast-moving, and high-impact innovation team.
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