Devops Engineer
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Overview
Job Description
Job Description: We are seeking a highly skilled DevOps / Site Reliability Engineer (SRE) with experience supporting modern AI platforms and cloud-native infrastructure. This role will focus on building scalable, reliable infrastructure for AI workloads while partnering closely with security and engineering teams to operationalize findings from an emerging AI-driven security platform used to identify code vulnerabilities and infrastructure risks.
This position is ideal for an engineer who enjoys automating infrastructure, improving software delivery pipelines, and supporting the rapid adoption of AI technologies in enterprise environments.
Responsibilities:
- Design, build, and maintain highly available infrastructure supporting AI and machine learning platforms.
- Develop scalable platform engineering solutions that enable reliable deployment and operation of AI services.
- Partner with development and security teams to remediate vulnerabilities and infrastructure issues identified by the AI driven security platform.
- Improve platform reliability through automation, monitoring, observability, and proactive performance tuning.
- Build and maintain robust CI/CD pipelines for application and infrastructure deployments.
- Automate operational workflows using Python and Infrastructure-as-Code practices.
- Implement DevSecOps best practices throughout the software development lifecycle.
- Support containerized workloads and cloud-native applications.
- Troubleshoot production issues, perform root cause analysis, and implement long-term reliability improvements.
- Optimize deployment strategies, release automation, and infrastructure scalability.
- Collaborate with AI engineering teams to ensure AI services are secure, resilient, and production-ready.
Required Qualifications:
- 5+ years of experience in DevOps, Site Reliability Engineering, or Platform Engineering
- Strong experience designing and maintaining CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins, Azure DevOps, or similar)
- Strong Python scripting and automation skills
- Experience supporting cloud infrastructure (AWS, Azure, or GCP)
- Experience with Infrastructure as Code (Terraform, CloudFormation, or Pulumi)
- Hands-on experience with Docker and Kubernetes
- Strong understanding of Linux systems administration
- Experience implementing monitoring and observability solutions (Prometheus, Grafana, Datadog, Splunk, etc.)
- Experience working with security scanning tools and vulnerability remediation
- Familiarity with DevSecOps principles and secure software delivery
- Experience supporting AIplatform engineering or machine learning infrastructure
Preferred Qualifications:
- Understanding of AI model deployment, inference infrastructure, and scalability considerations
- Familiarity with GPU-enabled infrastructure and AI compute environments
- Knowledge of vector databases, LLM deployment, or MLOps concepts
- Experience with Kubernetes operators, service mesh, or distributed systems
- Exposure to AI security tooling
- Experience integrating automated security scanning into CI/CD pipelines
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