6 months ago
New York, NY, USAMid Level
Responsibilities
- Manage Kubernetes clusters across multiple environments and regions.
- Own infrastructure as code for all resources.
- Maintain and improve CI/CD pipelines and GitOps-based deployments.
- Maintain and optimize real-time data pipelines processing billions of events per day across distributed queues and stream processors.
- Build monitoring, alerting, and observability capabilities.
- Debug production issues across services.
- Manage cloud costs and capacity planning.
- Own the company’s infrastructure while collaborating closely with a small engineering team.
Requirements
- Approximately 3+ years of experience in a DevOps or platform engineering role in production environments.
- Experience designing and operating large-scale distributed systems, including API design, reliability, and performance at scale.
- Strong experience with Kubernetes in a managed cloud environment.
- Proficiency with Terraform or a similar infrastructure-as-code tool.
- Experience with GitOps-based deployment workflows.
- Experience building or maintaining observability stacks for logging, metrics, and alerting.
- Experience handling production incidents calmly and methodically.
- Preferred experience includes multi-region deployments, search infrastructure, streaming or warehousing data pipelines, and proxy or networking infrastructure at scale.
Benefits
- NYC-based role with close collaboration with the engineering team and short feedback loops.
- Full ownership of the company’s infrastructure and exposure to real scaling challenges.
Tech Stack
Categories
Data EngineeringDevOps
About Tavily
Tavily builds a specialized web search and retrieval API for developers and enterprises to ground LLMs and AI agents with fresh, trustworthy online data. Its platform powers Retrieval-Augmented Generation and real-time reasoning for use cases like research copilots, sales assistants, and internal knowledge tools. Founded in 2024 and based in New York, Tavily operates as part of Nebius, focusing on connecting AI systems to current web content at scale.