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описание
Are you ready to work in the US time zone (EST-PST)?
Improvado is an AI-powered marketing intelligence platform trusted by enterprise brands. The company has raised $34M in Series A funding and is scaling its infrastructure to support AI-driven analytics and data processing.
задачи
Manage and evolve cloud infrastructure on AWS/Azure with Kubernetes, ensuring cluster reliability through capacity planning, autoscaling, incident response, and post-mortems;
Design and architect scalable, reliable infrastructure for AI-driven analytics and data processing at scale;
Build and maintain Helm charts, Terraform, and multi-environment setups;
Own monitoring and alerting across the stack using Prometheus/Mimir, Grafana, and CloudWatch;
Administer and optimize PostgreSQL, ClickHouse, and Redis storage;
Support RabbitMQ, AWS SQS, and Temporal message brokers;
Drive infrastructure security through secrets management, IAM policies, vulnerability scanning, and encryption;
Own network design and configuration, including VPCs, subnets, firewalls, load balancers, and VPNs;
Monitor and optimize cloud costs by identifying waste, right-sizing resources, and reporting on spend efficiency;
Participate in the on-call rotation.
требования
5+ Years of experience in a DevOps/SRE role;
Solid hands-on experience with most of the stack, including AWS/Azure/GCP, Kubernetes, Helm charts, GitHub CI, Terraform, PostgreSQL, ClickHouse, Redis, RabbitMQ, AWS SQS, Temporal, Prometheus/Mimir, Grafana, and CloudWatch;
Practical experience with AI-assisted development, including Claude Code and agentic coding, with the ability to validate AI-generated code;
Strong Linux fundamentals;
Bash and/or Python scripting skills;
Comfort with ambiguity and rapidly shifting priorities;
Detail-oriented and accountable when working with high-volume, high-stakes systems;
Availability in the EST timezone;
Fluent in English;
Nice to have: Golang or Python development background, HashiStack including Vault and Packer, ClickHouse administration, experience supporting data-intensive pipelines.
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