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Sre

  • Sre job type Posted on: Oct 14, 2026
  • Sre experience level Openkyber
  • Sre employment type Atlanta, Georgia
  • Sre employment type Remote
  • Sre salary Full-time

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Job Title :

Sre

Job Type :

Full-Time

Job Location :

Atlanta Georgia United States

Remote :

Yes

Jobcon Logo Job Description :

Need Only Machine Learning Engineer consultants. Please dont share AI/ML Engineer profiles. Experience Level: 12+ years. Job Title: Machine Learning Engineer (SRE Focus) Location: Remote Duration: Long Term Job Summary: Experienced Machine Learning Engineer with a strong Site Reliability Engineering (SRE) mindset to join our team. The candidate will have hands-on experience maintaining applications on both Windows and Linux environments, managing on-premises servers, and working with Kubernetes clusters. This role requires solid Python programming skills, a good understanding of machine learning concepts, and practical knowledge of ML model deployment, monitoring, and debugging. Key Responsibilities: Maintain and support machine learning applications running on Windows and Linux servers in on-premises environments. Manage and troubleshoot Kubernetes clusters hosting ML workloads. Collaborate with data scientists and engineers to deploy machine learning models reliably and efficiently. Implement and maintain monitoring and alerting solutions using DataDog to ensure system health and performance. Debug and resolve issues in production environments using Python and monitoring tools. Automate operational tasks to improve system reliability and scalability. Ensure best practices in security, performance, and availability for ML applications. Document system architecture, deployment processes, and troubleshooting guides. Required Qualifications: Proven experience working with Windows and Linux operating systems in production environments. Hands-on experience managing on-premises servers and Kubernetes clusters and Docker containers Strong proficiency in Python programming. Solid understanding of machine learning concepts and workflows. Experience with machine learning model deployment and lifecycle management. Familiarity with monitoring and debugging tools, e.g. DataDog. Ability to troubleshoot complex issues in distributed systems. Experience with CI/CD pipelines for ML applications. Familiarity with AWS cloud platforms Background in Site Reliability Engineering or DevOps practices. Strong problem-solving skills and attention to detail. Excellent communication and collaboration skills. We need an engineer who is also familiar with model development

For applications and inquiries,

Jobcon Logo Position Details

Posted:

Oct 14, 2026

Reference Number:

93396-38381

Employment:

Full-time

City:

Atlanta

Job Origin:

CIEPAL_ORGANIC_FEED

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Need Only Machine Learning Engineer consultants. Please dont share AI/ML Engineer profiles. Experience Level: 12+ years. Job Title: Machine Learning Engineer (SRE Focus) Location: Remote Duration: Long Term Job Summary: Experienced Machine Learning Engineer with a strong Site Reliability Engineering (SRE) mindset to join our team. The candidate will have hands-on experience maintaining applications on both Windows and Linux environments, managing on-premises servers, and working with Kubernetes clusters. This role requires solid Python programming skills, a good understanding of machine learning concepts, and practical knowledge of ML model deployment, monitoring, and debugging. Key Responsibilities: Maintain and support machine learning applications running on Windows and Linux servers in on-premises environments. Manage and troubleshoot Kubernetes clusters hosting ML workloads. Collaborate with data scientists and engineers to deploy machine learning models reliably and efficiently. Implement and maintain monitoring and alerting solutions using DataDog to ensure system health and performance. Debug and resolve issues in production environments using Python and monitoring tools. Automate operational tasks to improve system reliability and scalability. Ensure best practices in security, performance, and availability for ML applications. Document system architecture, deployment processes, and troubleshooting guides. Required Qualifications: Proven experience working with Windows and Linux operating systems in production environments. Hands-on experience managing on-premises servers and Kubernetes clusters and Docker containers Strong proficiency in Python programming. Solid understanding of machine learning concepts and workflows. Experience with machine learning model deployment and lifecycle management. Familiarity with monitoring and debugging tools, e.g. DataDog. Ability to troubleshoot complex issues in distributed systems. Experience with CI/CD pipelines for ML applications. Familiarity with AWS cloud platforms Background in Site Reliability Engineering or DevOps practices. Strong problem-solving skills and attention to detail. Excellent communication and collaboration skills. We need an engineer who is also familiar with model development

For applications and inquiries,

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