Machine Learning Engineer

Aziro Technologies LLC; United States; Remote; USD 120,000 - 170,000; Full time

Job summary

Aziro Technologies LLC is seeking a Machine Learning Engineer with a strong Site Reliability Engineering mindset to join our remote team. The role focuses on deploying and maintaining ML applications across Windows and Linux on-premises environments, with Kubernetes clusters and Python-based tooling.

You will collaborate with data scientists and engineers, implement monitoring with DataDog, and drive reliability through CI/CD pipelines, automation, and best-practice security and performance.

Qualifications

• Windows and Linux production environments experience.
• On-premises servers management and Kubernetes clusters.
• Strong Python programming skills.
• Understanding of ML concepts and workflows.
• Experience with ML model deployment and lifecycle management.
• Familiarity with monitoring and debugging tools such as DataDog.
• Experience with CI/CD pipelines for ML apps.
• Familiarity with AWS cloud platforms.
• Background in Site Reliability Engineering or DevOps.
• Strong problem-solving and attention to detail.
• Excellent communication and collaboration skills.

Responsibilities

• Maintain and support ML applications on Windows and Linux in on‑premises environments.
• Manage and troubleshoot Kubernetes clusters hosting ML workloads.
• Collaborate with data scientists and engineers to deploy ML models reliably.
• Implement and maintain monitoring and alerting using DataDog.
• Debug production issues with Python and monitoring tools.
• Automate operational tasks to improve reliability and scalability.
• Ensure security, performance, and availability for ML apps.
• Document system architecture and deployment processes.

Skills

Python programming

DevOps mindset

Problem-solving

Collaboration

CI/CD understanding

Tools

Kubernetes

Docker

DataDog

Git CI/CD

AWS

Job description

Job Title: Machine Learning Engineer (SRE Focus)

Location: Remote

Job Summary:

We are seeking a skilled Machine Learning Engineer with a strong Site Reliability Engineering (SRE) mindset to join our team. The ideal 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