SRE Engineer

Cogniify • Artificial Intelligence • MLOps • On-call duty • 1 month ago
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Location
Hybrid
Seniority
Middle · 4+ years exp

First seen by Alion on Sep 6, 2026.

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Cogniify

Cogniify is an artificial intelligence startup that provides custom software development, enterprise generative AI, and AI-enabled digital transformation solutions. Headquartered in Danville, California, the company helps organizations modernize their technology stacks and integrate machine learning into core business operations. Through cloud-native application engineering, intelligent analytics, and automated workflows, it enables clients to accelerate innovation and optimize operational efficiency.
cogniify.ai • HQ: Danville, United States • MLOps • Custom Software Development • AI Consulting & Integration • 51-200 employees • Est. 2024
Tech stack of Cogniify

We are looking for an experienced SRE Engineer to ensure the reliability, scalability, and performance of large-scale distributed systems in a production environment.

Responsibilities:

  • Manage and optimise production environments across AWS, Kubernetes, Docker, and Linux/Unix.
  • Build and maintain CI/CD pipelines and Infrastructure as Code using Terraform and Ansible.
  • Implement observability and monitoring using Prometheus, Grafana, OpenTelemetry, or Datadog.
  • Set up SLO-based alerting and incident detection mechanisms.
  • Troubleshoot production issues and participate in incident response.
  • Improve system reliability, availability, scalability, and performance.
  • Participate in the production on-call rotation, including some weekends approximately once every 2-3 weeks.

Requirements:

  • 4-6 years of experience in SRE, DevOps, or Production Engineering.
  • Strong hands-on experience with AWS, Kubernetes, Terraform, Grafana, and Prometheus.
  • Strong knowledge of Linux/Unix and Docker.
  • Experience with CI/CD and Infrastructure as Code.
  • Hands-on experience with observability, monitoring, alerting, and SLOs.
  • Experience working with large-scale distributed systems in production.
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