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Lineate

Custom AI for enterprise software delivery

Infrastructure Engineer

Site Reliability EngineeringPosted on Nov 2
Full-timeGeorgiaNo salary listed

About the Job

Requirements

  • At least 3 years of hands-on Linux system administration experience.
  • At least 2 years of hands-on AWS experience in a production environment.
  • Working familiarity with Kubernetes, including operating workloads, reading logs, and performing standard actions by following playbooks; deep cluster-design expertise is not required.
  • Working familiarity with AWS Databricks, including running jobs, managing clusters, and troubleshooting using existing playbooks; deep platform expertise is not required.
  • Practical experience using AI agents or large language model-based tools, such as Claude, ChatGPT, MCP-based agents, Cursor, or Copilot, to automate operational, scripting, or support tasks.
  • At least 1 year of Windows system administration experience.
  • At least 1 year of practical scripting experience in one or more of Python, Bash, or Go.
  • Ability to maintain existing pipelines in CI/CD tools such as Jenkins, GitHub Actions, GitLab CI, or equivalent.
  • Proficiency with Git and standard version-control workflows.
  • Hands-on experience with Docker and container workflows.
  • Experience managing and maintaining production IT infrastructure.
  • Comfort working with a broad range of open-source technologies and tools.

Responsibilities

  • Participate in a 24/7 shift schedule.
  • Monitor systems and respond to automated alerts in line with support SLAs.
  • Diagnose and resolve system problems.
  • Operate Kubernetes workloads and Databricks resources by following team playbooks and runbooks.
  • Perform routine system administration on Linux and Windows servers.
  • Respond to customer inquiries via Slack, email, or Zendesk.
  • Document, categorize, and prioritize incoming support requests.
  • Communicate the status of open requests clearly and regularly to customers.
  • Identify and implement automation opportunities, including AI-agent-driven automation of ticket triage, runbook execution, log analysis, and knowledge-base maintenance.
  • Coordinate incident triage, implement mitigation measures, conduct root cause analysis, and lead post-incident reviews.
  • Maintain and improve runbooks, playbooks, FAQs, and internal knowledge bases.
  • Implement and improve observability, including metrics, logs, traces, dashboards, alerting, and SLO tracking.
  • Contribute to CI/CD pipelines and basic infrastructure automation.
  • Follow information security practices, including IAM, secrets handling, and least privilege.
  • Participate in improving support and infrastructure processes.

Desired Qualifications

  • Experience with Infrastructure as Code tools such as Terraform, Ansible, or CloudFormation.
  • Experience with observability stacks such as Prometheus, Grafana, ELK/Loki, Datadog, CloudWatch, or equivalent, including building dashboards and alerts.
  • Knowledge of networking fundamentals, including TCP/IP, DNS, HTTP(S), TLS, VPN, load balancers, VPC design, firewalls, and routing.
  • Knowledge of cloud security, including IAM, KMS, secrets management tools such as AWS Secrets Manager or HashiCorp Vault, least-privilege design, and audit logging.
  • Experience with PostgreSQL, MySQL, or Redis database operations, including backups, restores, basic tuning, and monitoring.
  • Familiarity with incident-management practices, including on-call rotations, runbooks, SLI/SLO/SLA, and blameless postmortems.
  • Familiarity with data-platform concepts such as Spark/PySpark basics and ETL pipelines in the context of Databricks.
  • Experience writing and maintaining MCP servers, prompt-based automations, or AI-driven runbooks.
  • Exposure to GCP or Azure in addition to AWS.

About the Company

Lineate

Custom AI for enterprise software delivery

What Lineate does: it provides AI-enabled software solutions, including Intelligent Document Processing (IDP), Agentic AI Development, and AI-powered Engineering Consulting, with a focus on delivering measurable business value. How its products work: IDP integrates with existing workflows to extract, classify, and route data from unstructured documents; AI agents act on business rules with contextual intelligence; GenAI-informed engineering embeds AI across the software lifecycle. How it differs from competitors: it emphasizes custom, fast-built AI tailored to client context, a data-focused problem-solving approach, and engineers empowered to build scalable systems on complex projects. The goal: help clients automate, accelerate, or innovate with purpose and precision to achieve real-world results and better operational efficiency.

Company size
51-200
Company stage
Not listed
Total funding
Not listed
Headquarters
New York City, New York
Founded
2005

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